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Pages: 224"],"prefix":"10.1002","volume":"2","author":[{"given":"John","family":"Cubbin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2006,9,20]]},"container-title":["Journal of Forecasting"],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Ffor.3980020209","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.3980020209","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T19:40:17Z","timestamp":1697744417000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/for.3980020209"}},"issued":{"date-parts":[[1983,4]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[1983,4]]}},"alternative-id":["10.1002\/for.3980020209"],"URL":"https:\/\/doi.org\/10.1002\/for.3980020209","archive":["Portico"],"ISSN":["0277-6693","1099-131X"],"issn-type":[{"value":"0277-6693","type":"print"},{"value":"1099-131X","type":"electronic"}],"published":{"date-parts":[[1983,4]]}},{"indexed":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T10:38:23Z","timestamp":1698489503329},"reference-count":25,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2006,9,22]],"date-time":"2006-09-22T00:00:00Z","timestamp":1158883200000},"content-version":"vor","delay-in-days":6383,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Forecasting"],"published-print":{"date-parts":[[1989,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Often a forecaster has supplementary information (e.g. field reports or forecasts from another source) that cannot be included directly in a time series model. Especially interesting are cases where this information is given at time intervals that are different from those of the time series model forecasts. Previous authors have considered a numerical and a model\u2010based statistical method for combining extra\u2010model information of this type with ARIMA model forecasts. This paper extends both methods to vector ARMA model forecasts and dynamic regression (transfer function) model forecasts. It is also shown that a Lagrange multiplier numerical procedure arises as a special case of the model\u2010based procedure. 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Data exploration, model identification and estimation, and interpretation of final forecasts are made considerably easier by the visual relay of information. This article discusses some recent developments in time series graphics designed to assist in the forecasting process. A discussion of requirerients for effective use of graphics in interactive forecasting is included as illustrated through an application of the Box\u2010Jenkins methodology. Illustrations are included from the STATGRAPHICS system, a prototype implementation in APL.<\/jats:p>","DOI":"10.1002\/for.3980010407","type":"journal-article","created":{"date-parts":[[2007,7,7]],"date-time":"2007-07-07T21:50:35Z","timestamp":1183845035000},"page":"397-408","source":"Crossref","is-referenced-by-count":2,"title":["Requirements for effective use of graphical methods in interactive forecasting"],"prefix":"10.1002","volume":"1","author":[{"given":"Neil W.","family":"Polhemus","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2006,9,20]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.2307\/2683467"},{"key":"e_1_2_1_3_1","volume-title":"Time Series Analysis Forecasting and Control","author":"Box G. E. P.","year":"1970"},{"key":"e_1_2_1_4_1","volume-title":"Time Series Analysis, Surveys and Recent Developments","author":"Cleveland N. 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F. \u2018Computer graphics available to statisticians\u2019 Proceedings of the Computer Science and Statistics Symposium on the Interface 1978 pp.93\u2013100."}],"container-title":["Journal of 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Forecast accuracy, measured as the MAPE for the one step ahead forecast, is discussed for different series lengths.<\/jats:p>","DOI":"10.1002\/for.3980030311","type":"journal-article","created":{"date-parts":[[2007,7,8]],"date-time":"2007-07-08T00:15:23Z","timestamp":1183853723000},"page":"329-332","source":"Crossref","is-referenced-by-count":19,"title":["A comparative arima analysis of the 111 series of the makridakis competition"],"prefix":"10.1002","volume":"3","author":[{"given":"Edward J.","family":"Lusk","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joao S.","family":"Neves","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2006,9,21]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.2307\/1271080"},{"key":"e_1_2_1_3_1","unstructured":"Hill G. andFildes R. \u2018Automatic Box\u2010Jenkins forecasting and the Makridakis competition\u2019 paper presented at the Third International Symposium on Forecasting Philadelphia 1983."},{"key":"e_1_2_1_4_1","volume-title":"User's Manual for IDA","author":"Ling R. 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This paper discusses the problem of misspecified prediction when a conjectured spectral density <jats:italic>f<\/jats:italic><jats:sub>\u03b8<\/jats:sub>(\u03bb), \u03b8\u2208\u0398, is fitted to <jats:italic>g<\/jats:italic>(\u03bb). Then, constructing the best linear predictor based on <jats:italic>f<\/jats:italic><jats:sub>\u03b8<\/jats:sub>(\u03bb), we can evaluate the prediction error <jats:italic>M<\/jats:italic>(\u03b8). Since \u03b8 is unknown we estimate it by a quasi\u2010MLE <jats:styled-content>$\\hat{\\theta}_{Q}$<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/tex2gif-ueqn-1.gif\" xlink:title=\"equation image\" \/><\/jats:styled-content>. The second\u2010order asymptotic approximation of <jats:styled-content>$M(\\hat{\\theta}_{Q})$<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/tex2gif-ueqn-2.gif\" xlink:title=\"equation image\" \/><\/jats:styled-content> is given. This result is extended to the case when <jats:italic>X<\/jats:italic><jats:sub><jats:italic>t<\/jats:italic><\/jats:sub> contains some trend, i.e. a time series regression model. These results are very general. Furthermore we evaluate the second\u2010order asymptotic approximation of <jats:styled-content>$M(\\hat{\\theta}_{Q})$<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/tex2gif-ueqn-3.gif\" xlink:title=\"equation image\" \/><\/jats:styled-content> for a time series regression model having a long\u2010memory residual process with the true spectral density <jats:italic>g<\/jats:italic>(\u03bb). Since the general formulae of the approximated prediction error are complicated, we provide some numerical examples. Then we illuminate unexpected effects from the misspecification of spectra. Copyright \u00a9 2001 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/for.807","type":"journal-article","created":{"date-parts":[[2002,9,10]],"date-time":"2002-09-10T15:25:40Z","timestamp":1031671540000},"page":"543-564","source":"Crossref","is-referenced-by-count":3,"title":["Misspecified prediction for time series"],"prefix":"10.1002","volume":"20","author":[{"given":"In\u2010Bong","family":"Choi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masanobu","family":"Taniguchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2001,12,17]]},"reference":[{"key":"e_1_2_1_2_1","volume-title":"The Statistical Analysis of Time Series","author":"Anderson TW","year":"1971"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/01621459.1979.10481635","article-title":"The asymptotic mean square error of multistep prediction from the regression model with autoregressive errors","volume":"74","author":"Baillie RT","year":"1979","journal-title":"J. Amer. Statist. Assoc."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.2307\/2287516"},{"key":"e_1_2_1_5_1","volume-title":"Time Series; Data Analysis and Theory","author":"Brillinger DR","year":"1975"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176347393"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/00131649921970134"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.3060405"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/68.1.165"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1034276623"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176345696"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1080\/01621459.1985.10478208","article-title":"Properties of predictors in misspecified autoregressive time series models","volume":"80","author":"Kunitomo N","year":"1985","journal-title":"J. Amer. Statist. Assoc."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.2307\/3212924"},{"key":"e_1_2_1_14_1","unstructured":"RozanovYA.1969.On a new class of statistical estimates. Soviet\u2013Japanese Symposium Theory of Probability Novosibirsk239\u2013252."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02480345"},{"key":"e_1_2_1_16_1","first-page":"311","volume-title":"Recent Development of Statistical Inference and Data Analysis","author":"Taniguchi M","year":"1980"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176347975"},{"key":"e_1_2_1_18_1","first-page":"1","article-title":"Semiparametric estimation and its applications for time series","volume":"59","author":"Yajima Y","year":"1994","journal-title":"Keizaigaku Ronsyu"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346680"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02481980"}],"container-title":["Journal of Forecasting"],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Ffor.807","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.807","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T07:57:57Z","timestamp":1697615877000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/for.807"}},"issued":{"date-parts":[[2001,12]]},"references-count":19,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2001,12]]}},"alternative-id":["10.1002\/for.807"],"URL":"https:\/\/doi.org\/10.1002\/for.807","archive":["Portico"],"ISSN":["0277-6693","1099-131X"],"issn-type":[{"value":"0277-6693","type":"print"},{"value":"1099-131X","type":"electronic"}],"published":{"date-parts":[[2001,12]]}},{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:43:43Z","timestamp":1772761423995,"version":"3.50.1"},"reference-count":25,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2006,11,2]],"date-time":"2006-11-02T00:00:00Z","timestamp":1162425600000},"content-version":"vor","delay-in-days":4902,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Forecasting"],"published-print":{"date-parts":[[1993,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We analyse the price movement of the S&amp;P 500 futures market for violations of the efficient market hypothesis on a short\u2010term basis. To assess market inefficiency we construct a model and find that the returns, i.e. the difference in the logarithm of closing prices on consecutive days, exhibit the usual conditional heteroscedasticity behaviour typical of long series of financial data. To account for this non\u2010linear behaviour we scale the returns by a volatility factor which depends on the daily high, low, and closing price. The rescaled series, which may be interpreted as the trend\u2010countertrend component of the time series, is modelled using Box and Jenkins techniques. The resulting model is an ARMA(1,1). The scale factors are assumed to form a time series and are modelled using a semi\u2010non\u2010parametric method which avoids the restrictive assumptions of most ARCH or GARCH models. Using the combined model we perform 1000 simulations of market data, each simulation comprising 250 days (approximately one year). We then formulate a naive trading strategy which is based on the ratio of the one\u2010day\u2010ahead expected return to its one\u2010day\u2010ahead expected conditional standard deviation. The trading strategy has four adjustable parameters which are set to maximize profits for the simulation data. Next, we apply the trading strategy to one year of recent out\u2010of\u2010sample data. Our conclusion is that the S&amp;P 500 futures market exhibits only slight inefficiencies, but that there exist, in principle, better trading strategies which take account of risk than the benchmark strategy of buy\u2010and\u2010hold. We have also constructed a linear model for the return series. Using the linear model, we have simulated returns and determined the optimum values for the adjustable parameters of the trading strategy. In this case, the optimum trading strategy is the same as the benchmark strategy, buy\u2010and\u2010hold. Finally, we have compared the profitability of the optimized trading strategy, based on the non\u2010linear model, to three <jats:italic>ad hoc<\/jats:italic> trading strategies using the out\u2010of\u2010sample data. The three <jats:italic>ad hoc<\/jats:italic> strategies are more profitable than the optimized strategy.<\/jats:p>","DOI":"10.1002\/for.3980120503","type":"journal-article","created":{"date-parts":[[2007,7,8]],"date-time":"2007-07-08T19:37:07Z","timestamp":1183923427000},"page":"395-420","source":"Crossref","is-referenced-by-count":3,"title":["Assessing inefficiency in the s&amp;p 500 futures market"],"prefix":"10.1002","volume":"12","author":[{"given":"C. H.","family":"Farrell","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E. A.","family":"Olszewski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2006,11,2]]},"reference":[{"key":"e_1_2_1_2_1","volume-title":"The Dow Jones Irwin Guide to Trading Systems","author":"Babcock B.","year":"1989"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/758534691"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(86)90063-1"},{"issue":"41","key":"e_1_2_1_5_1","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1086\/261703","article-title":"Noise trader risk in financial markets","volume":"98","author":"Delong J. B.","year":"1990","journal-title":"Journal of Political Economy"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.2307\/2325486"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1086\/261535"},{"key":"e_1_2_1_8_1","unstructured":"Fortune P. \u2018Stock market efficiency: an autopsy\u2019 New England Economic Review Federal Reserve Bank of Boston (1991) March\/April 17\u201340."},{"key":"e_1_2_1_9_1","volume-title":"Nonparametric and Semi nonparametric Method in Econometric Statistics, Proceedings of the Fifth International Symposium in Economic Theory and Econometrics","author":"Gallant A. R.","year":"1989"},{"key":"e_1_2_1_10_1","unstructured":"Gallant A. R. Rossi P. E.andTauchen G. \u2018Stock prices and volume\u2019 Working paper Department of Statistics North Carolina State University January1990."},{"key":"e_1_2_1_11_1","unstructured":"Gallant A. 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A reappraisal of empirical evidence\u2019 Working paper No. 2795 National Bureau of Economic Research Cambridge MA December1988.","DOI":"10.3386\/w2795"},{"key":"e_1_2_1_16_1","first-page":"29","article-title":"Capital market efficiency: an update","volume":"2","author":"LeRoy S. F.","year":"1990","journal-title":"Economic Review, Federal Reserve Bank of San Francisco"},{"key":"e_1_2_1_17_1","volume-title":"Times Series Techniques for Economists","author":"Mills T. C.","year":"1990"},{"key":"e_1_2_1_18_1","volume-title":"Multivariate Statistical Methods","author":"Morrison D. 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The empirical findings indicate that the time\u2010varying contributions of six aspects of governance quality on nonrenewable (renewable) energy consumption predicting vary greatly in E\u20107 and G\u20107 countries. The time\u2010varying contribution of governance quality within countries is heterogeneous and asymmetrical, especially India (Germany) in E\u20107 countries (G\u20107 countries). The prediction contribution distribution of governance quality between countries is more discrete in G\u20107 countries than E\u20107 countries. 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(Working Paper No.55).Minneapolis MN: Federal Reserve Bank of Minneapolis."}],"container-title":["Journal of Forecasting"],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.2658","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/for.2658","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.2658","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T14:00:56Z","timestamp":1694008856000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/for.2658"}},"issued":{"date-parts":[[2020,2,16]]},"references-count":22,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,8]]}},"alternative-id":["10.1002\/for.2658"],"URL":"https:\/\/doi.org\/10.1002\/for.2658","archive":["Portico"],"ISSN":["0277-6693","1099-131X"],"issn-type":[{"value":"0277-6693","type":"print"},{"value":"1099-131X","type":"electronic"}],"published":{"date-parts":[[2020,2,16]]},"assertion":[{"value":"2018-07-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-01-03","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-02-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]},{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T15:05:52Z","timestamp":1747148752024,"version":"3.40.5"},"reference-count":32,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T00:00:00Z","timestamp":1707868800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Forecasting"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Suppose an underlying multivariate time series is contemporaneously aggregated under a known aggregation mechanism, and a lower dimensional multivariate aggregated time series is obtained. 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To this end, we introduce a forecasting measure to quantify the advantages of using contemporaneous aggregation in forecasting in the sense of the mean\u2010squared error. The forecasting measure is constructed under the assumption that the underlying time series follows the vector autoregressive moving average (VARMA) process. The estimation procedure does not require specifying any particular form of the VARMA, namely, the lag order \n and \n. 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Since the first quantitative studies of the \u2018predicament of mankind\u2019 at the end of the 1960s, large\u2010scale forecasting models have attracted substantial criticism on the grounds of failing both to inform the policy process and to embrace the notion of surprise\u2010rich futures. Almost all attempts at developing models of environmental systems, at whatever scale, have been dogged by the sceptic's question: Is the model capable of predicting conditions substantially different from those observed in the past? The question, strictly speaking, is not answerable. The paper defines therefore three approaches to the development of modelsthe mechanical, metric, and linguistic paradigmsand constructs thence a reorientation of the customary problem of prediction in order to explore the concept of reachable, and radically different, futures. Such reorientation depends crucially on a juxtaposition of the linguistic and mechanical descriptions of a system's behaviour. The nine papers of this special issue are then summarized in the context provided by this reconsideration of how we formalize our thinking about the behaviour of systems, and of what might be meant by the systematic analysis of reachable futures.<\/jats:p>","DOI":"10.1002\/for.3980100103","type":"journal-article","created":{"date-parts":[[2007,7,8]],"date-time":"2007-07-08T12:50:22Z","timestamp":1183899022000},"page":"3-19","source":"Crossref","is-referenced-by-count":24,"title":["Forecasting environmental change"],"prefix":"10.1002","volume":"10","author":[{"given":"M. B.","family":"Beck","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2006,11,2]]},"reference":[{"key":"e_1_2_1_2_1","unstructured":"ApSimon H. M.andWilson J. J. N. \u2018The application of numerical models to assess dispersion and deposition in the event of a nuclear accident\u2019.J. 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Forecasting this issue."},{"key":"e_1_2_1_23_1","unstructured":"Rose K. A. Smith E. P. Gardner R. H. Brenkert A. L.andBartell S. M. \u2018Parameter sensitivities Monte Carlo filtering and model forecasting under uncertainty\u2019 J. Forecasting this issue."},{"key":"e_1_2_1_24_1","first-page":"97","volume-title":"Computer Applications in Fermentation Technology: Modelling and Control of Biotechnical Processes, Preprints","author":"Stephanopoulos G.","year":"1988"},{"key":"e_1_2_1_25_1","unstructured":"van Straten G.andKessman K. J.\u2018Uncertainty propagation and speculation in projective forecasts of environmental change: a lake\u2010eutrophication example\u2019 J. Forecasting this issue."},{"key":"e_1_2_1_26_1","unstructured":"Wolock D. M.andHornberger G. M. \u2018Hydrological effects of changes in levels of atmospheric carbon dioxide\u2019 J. Forecasting this issue."},{"key":"e_1_2_1_27_1","unstructured":"Young P. C. Ng C. N. Lane K.andParker D. \u2018Recursive forecasting smoothing and seasonal adjustment of non\u2010stationary environmental data\u2019 J. 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Such modifications allow current period information to be incorporated into the forecast value, and ensure that the forecast is realistic in the context of current industry trends. This paper investigates the potential value of this approach in production forecasting in the Australian lamb industry. Several individual and composite econometric models were used to forecast a lamb\u2010slaughtering series with a selected forecast being given to a panel of lamb industry specialists for consideration and modification. The results demonstrate that this approach offers considerable accuracy advantages in the short\u2010term forecasting of livestock market variables, such as slaughtering, whose values can be strongly influenced by current industry conditions.<\/jats:p>","DOI":"10.1002\/for.3980140505","type":"journal-article","created":{"date-parts":[[2007,7,9]],"date-time":"2007-07-09T00:21:02Z","timestamp":1183940462000},"page":"453-464","source":"Crossref","is-referenced-by-count":5,"title":["Modifying quantitative forecasts of livestock production using expert judgments: An application to the australian lamb industry"],"prefix":"10.1002","volume":"14","author":[{"given":"D. T.","family":"Vere","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. 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W. beef production: an evaluation of alternative techniques","volume":"47","author":"Gellatly C.","year":"1979","journal-title":"Review of Marketing and Agricultural Economics"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/00036847300000003"},{"key":"e_1_2_1_9_1","volume-title":"Econometric Models, Techniques and Applications","author":"Intriligator M. D.","year":"1978"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1086\/260209"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-2070(85)80068-6"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2070(92)90004-S"},{"key":"e_1_2_1_13_1","unstructured":"Lim J. S.andO'Connor M. J. \u2018Judgmental adjustment of initial forecasts: its effects and biases\u2019 unpublished paper School of Information Systems the University of New South Wales Sydney Australia 1994."},{"key":"e_1_2_1_14_1","first-page":"307","article-title":"Forecasting accuracy and the assumption of constancy, OMEGA","volume":"9","author":"Makridakis S.","year":"1981","journal-title":"The International Journal of Management Science"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.3980080207"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2070(92)90026-6"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2070(93)90002-5"},{"key":"e_1_2_1_18_1","volume-title":"Applied Economic Forecasting","author":"Theil H.","year":"1966"},{"key":"e_1_2_1_19_1","first-page":"287","article-title":"Supply and demand interactions in the New South Wales prime lamb market","volume":"59","author":"Vere D. 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The Development and Application of a Quarterly Econometric Model of the Australian Prime Lamb Market NSW Agriculture Economic Services Unit Agricultural Economics Bulletin (1994) No. 11."}],"container-title":["Journal of Forecasting"],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Ffor.3980140505","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.3980140505","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T06:46:07Z","timestamp":1698302767000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/for.3980140505"}},"issued":{"date-parts":[[1995,9]]},"references-count":20,"journal-issue":{"issue":"5","published-print":{"date-parts":[[1995,9]]}},"alternative-id":["10.1002\/for.3980140505"],"URL":"https:\/\/doi.org\/10.1002\/for.3980140505","archive":["Portico"],"ISSN":["0277-6693","1099-131X"],"issn-type":[{"value":"0277-6693","type":"print"},{"value":"1099-131X","type":"electronic"}],"published":{"date-parts":[[1995,9]]}},{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:08:02Z","timestamp":1761293282318},"reference-count":26,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2015,6,9]],"date-time":"2015-06-09T00:00:00Z","timestamp":1433808000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Forecasting"],"published-print":{"date-parts":[[2015,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The importance of modelling correlation has long been recognised in the field of portfolio management, with large\u2010dimensional multivariate problems increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating a number of models used to generate forecasts of the correlation matrix for large\u2010dimensional problems. We find evidence in favour of assuming equicorrelation across various portfolio sizes, particularly during times of crisis. During periods of market calm, however, the suitability of the constant conditional correlation model cannot be discounted, especially for large portfolios. A portfolio allocation problem is used to compare forecasting methods. The global minimum variance portfolio and Model Confidence Set are used to compare methods, while portfolio weight stability and relative economic value are also considered. 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The importance of the PVF is tremendous, and it can be essential in optimizing the home appliances to maximize the Renewable Energy Sources (RES) usage or to create performant bids for the electricity market. Several use cases are considered from the connectivity point of view. Therefore, in this paper, we propose a Weather Prediction Error (WPE)\u2010based method that uses a Stacking Regressor (SR) for various PV systems that coexist in the emerging Energy Communities (EC) landscape. The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE. The forecast results of four types of PV systems from size and connectivity point of view are investigated. Compared with individual Machine Learning (ML) models, R<jats:sup>2<\/jats:sup> increases with more than 3% with the SR and with more than 6% after applying the adjustment coefficients. Nevertheless, the major improvement is recorded for the off\u2010grid inverter and for the large industrial PV power plant, demonstrating that the proposed model is suitable for these types of systems. The other metrics improved as well, especially Mean Average Error (MAE) that decreases between 10% and 23%. A significant decrease is in the case of the industrial on\u2010grid PV, from 130\u2009kW to 123\u2009kW using the SR and to 107\u2009kW after adjustments. This represents around 18% from the initial MAE. The ratio of daily deviations is also improved using the SR. For all the PV systems, the ratio stabilizes in a shorter interval, from daily values between 0.78 and 1.33 obtained with the ML models to values between 0.83 and 1.24 obtained with the SR. 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A novel supervised machine learning early classification technique (Artificial Intelligence) has been applied, for the first time, to the high\u2010frequency time series of both price and certain technical indicators.<\/jats:p><jats:p>The results obtained allow us to assert that the intraday movement of the Ibex 35 can be predicted with acceptable levels of accuracy 24\u2009min after the start of the session and to establish certain informative intraday hourly patterns. Consequently, different indicators of precision and earliness in the session are generated, obtaining that, after a certain point in the session, no gains in precision are generated.<\/jats:p>","DOI":"10.1002\/for.2933","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T05:53:34Z","timestamp":1668750814000},"page":"1150-1166","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Early prediction of Ibex 35 movements"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9731-496X","authenticated-orcid":false,"given":"I. 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A large number of models have already been proposed for this task, but they generally have the disadvantage of either estimating the model in a two\u2010step process, possibly losing efficiency, or relying on methods that are cumbersome for the practitioner to use. We instead propose using variational inference and the probabilistic programming library Pyro for estimating the model. This allows for flexibility in modelling assumptions while still being able to estimate the full model in one step. The models are fitted on Swedish mortality data, and we find that the in\u2010sample fit is good and that the forecasting performance is better than other popular models. Code is available online (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/LPAndersson\/VImortality\">https:\/\/github.com\/LPAndersson\/VImortality<\/jats:ext-link>\n                    ).\n                  <\/jats:p>","DOI":"10.1002\/for.70078","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T02:03:58Z","timestamp":1764986638000},"page":"1069-1076","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mortality Forecasting Using Variational Inference"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8866-3567","authenticated-orcid":false,"given":"Patrik","family":"Andersson","sequence":"first","affiliation":[{"name":"Uppsala University  Uppsala Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathias","family":"Lindholm","sequence":"additional","affiliation":[{"name":"Stockholm University  Stockholm Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1748499520000275"},{"key":"e_1_2_10_3_1","unstructured":"Archer E. 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Employing a combination of statistical and deep learning models, the study aims to predict both the mean and variance of stock price movements for select pharmaceutical companies in India based on their market capitalization. The forecasts are then utilized to assess the effectiveness of the Bollinger Band (BB) trading strategy in terms of hit ratio and average returns per trade. The study covers both pre\u2010 and post\u2010COVID periods. The results indicate that the integrated mean and volatility model employed in this study outperforms the stand\u2010alone mean and volatility models when back\u2010tested with BB trading strategies, leading to higher returns. Moreover, when combined with a volatility model, the integrated deep learning model consistently demonstrates superior performance compared with the standalone mean or volatility model. The integrated model has yielded significantly higher annualized average returns (&gt;\u2009200%) than the returns generated based on technical indicators, as suggested by existing studies. These findings have significant practical implications, providing investors and traders with an advanced alternative to conventional trading methods.<\/jats:p>","DOI":"10.1002\/for.70046","type":"journal-article","created":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T13:45:38Z","timestamp":1760795138000},"page":"563-588","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging an Integrated First and Second Moments Modeling Approach for Optimal Trading Strategies: Evidence From the Indian Pharma Sector in the Pre\u2010 and Post\u2010COVID\u201019 Era"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2117-3392","authenticated-orcid":false,"given":"Himanshu","family":"Kautkar","sequence":"first","affiliation":[{"name":"Quantzig AI Solutions Private Limited  Bengaluru India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudeep","family":"Das","sequence":"additional","affiliation":[{"name":"Boston Consulting Group  Gurugram India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Himanshi","family":"Gupta","sequence":"additional","affiliation":[{"name":"Infosys  Gurugram India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sajal","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Management Development Institute Gurgaon  Gurgaon India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kakali","family":"Kanjilal","sequence":"additional","affiliation":[{"name":"IMI Delhi  New Delhi India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,10,18]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.37394\/232015.2022.18.93"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/eufm.12326"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2307\/2330824"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.11.008"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/hhw024"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304\u20104076(86)90063\u20101"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.2585"},{"key":"e_1_2_9_9_1","first-page":"1","volume-title":"Time Series Analysis: Forecasting and Control","author":"Box G. 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Specifically, we integrate three well\u2010known filtering FS techniques (information gain [IG], principal component analysis [PCA], and relief [Re]) and three popular wrapper FS techniques (particle swarm optimization [PSO], genetic algorithm [GA], and artificial bee colony [ABC]) with the support vector machine (SVM) to generate our ensemble classifiers. We then assess the performance of each of the six ensemble classifiers to predict AEM and REM based on three criteria: type \u0399 error, type \u0399\u0399 error, and average accuracy. The results show that the ABC\u2010SVM ensemble classifier outperforms the others in predicting both AEM and REM. We also find that, overall, wrapper FS ensemble classifiers outperform filtering FS ensemble classifiers in predicting AEM and REM and that it is more difficult for our ensemble classifiers to predict REM than to predict AEM. This paper contributes to the literature on EM prediction by introducing six new ensemble classifiers. It is also the first work (to the best of our knowledge) in the domain of ensemble classifiers' applications (a) to consider both REM and AEM in one context and to show that REM is more difficult to predict than AEM and (b) to compare the performance of wrapper and filtering FS techniques in the EM prediction setting.<\/jats:p>","DOI":"10.1002\/for.2885","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T03:48:29Z","timestamp":1656647309000},"page":"1639-1660","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Predicting earnings management through machine learning ensemble classifiers"],"prefix":"10.1002","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8956-8131","authenticated-orcid":false,"given":"Ahmad","family":"Hammami","sequence":"first","affiliation":[{"name":"John Molson School of Business Concordia University  Montreal Qu\u00e9bec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6761-9946","authenticated-orcid":false,"given":"Mohammad","family":"Hendijani Zadeh","sequence":"additional","affiliation":[{"name":"Sobey School of Business Saint Mary's University  Halifax Nova Scotia Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,7,11]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.2308\/jeta-50390"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacceco.2014.11.004"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.accinf.2016.12.004"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/auar.12176"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1108\/JFRA-01-2019-0008"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcaf.22239"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.09.033"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10551-019-04176-x"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.5402\/2012\/426957"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.econmod.2014.12.035"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-38577-3_54"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.accinf.2018.11.004"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.accinf.2017.06.004"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1108\/02686900410524436"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.2308\/accr.2008.83.3.757"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacceco.2010.01.002"},{"key":"e_1_2_10_18_1","unstructured":"CPA Canada and AICPA. 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Although there are several different types of imaging techniques that can be used to represent related technical indicators, including line charts, candlestick charts, and bar charts, no one has yet examined the effect of using these types of image visualization techniques on the prediction performance of deep learning models. In this paper, three types of image patterns are compared, specifically, line charts with trading volume information represented by a bar chart, candlestick charts with trading volume information, and a mixed type of image with two other related technical indicators, that is, MACD and RSI. The experimental results that are based on data for six companies from different industries and with different scales of stock price fluctuation show that the mixed image pattern type allows 2\u2010D CNN and VGG16 to perform better than the other two image pattern types in terms of predicting the stock prices for the next day, week, and month. 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However, it is challenging to predict fire incidents and identify potential influencing factors due to limitations of data, model accuracy and interpretability. This paper proposes a novel scheme designed to enhance predictive and explainable capabilities by integrating multi\u2010source data, adaptive machine learning methods, and Shapley additive explanation (SHAP) tools for more effective and applicable fire safety management. The scheme shows satisfactory prediction results by leveraging the data from grid\u2010style management systems and our proposed machine learning method with dynamic time warping distance\u2010based time series clustering, significantly outperforming the methods merely based on time series modeling. Moreover, clustered features help to clarify the main influencing risk factors and provide clearer insights for model interpretability. With global SHAP, community clusters capturing community fire event frequency, as well as historical records on fire police rescue, smoke alarms, and fire alarms, are found to be significant risk factors among all the features over the whole communities and periods via the model interpretability analysis, implying that communities where fires used to occur frequently are more likely to occur in future, which should be highly vigilant in real fire management. With local SHAP, specific risk factors that vary across communities can be identified for any single community with a given period. We demonstrate the potential of this integrated machine learning scheme in improving the prediction accuracy and risk identification applicability of fire incidents, which contributes to more effective and customized fire safety management.<\/jats:p>","DOI":"10.1002\/for.3266","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T02:36:16Z","timestamp":1741746976000},"page":"1699-1715","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Fire Prediction and Risk Identification With Interpretable Machine Learning"],"prefix":"10.1002","volume":"44","author":[{"given":"Shan","family":"Dai","sequence":"first","affiliation":[{"name":"Shenzhen Research Institute of Big Data  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Research Institute of Big Data  Shenzhen China"},{"name":"The Chinese University of Hong Kong (Shenzhen)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhelin","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Statistics, School of Economics Shenzhen University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8145-4929","authenticated-orcid":false,"given":"Shipei","family":"Zeng","sequence":"additional","affiliation":[{"name":"Shenzhen Research Institute of Big Data  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2017.05.009"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1959.1104847"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.3389\/fenrg.2023.1284676"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.2307\/2985674"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_8_7_1","doi-asserted-by":"crossref","unstructured":"ChenT. andC.Guestrin.2016. \u201cXgboost: A Scalable Tree Boosting System.\u201d InProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/14498596.2009.9635168"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1745-5871.2009.00587.x"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2020.104802"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0379-7112(01)00033-9"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2019.104339"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10694\u2010023\u201001409\u20104"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2012.03.007"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2022.103738"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.firesaf.2014.08.015"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.2307\/2532444"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0379-7112(02)00049-8"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.firesaf.2019.102890"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2022.06.037"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1015330931387"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.firesaf.2013.07.002"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1136\/injuryprev-2018-043062"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2020.12.019"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2009.12.008"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10694\u2010023\u201001427\u20102"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijdrr.2022.103138"},{"key":"e_1_2_8_29_1","unstructured":"Lundberg S. 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As a first step to overcoming the conceptual and practical difficulties surrounding the estimation of\n                    <jats:italic>de facto<\/jats:italic>\n                    population, this paper relies on forecasting symptomatic variables to integrate existing direct and indirect methods into a unified theoretical framework for estimating overnight residents.\u2003Copyright \u00a9 2009 John Wiley &amp; Sons, Ltd.\n                  <\/jats:p>","DOI":"10.1002\/for.1166","type":"journal-article","created":{"date-parts":[[2009,12,1]],"date-time":"2009-12-01T22:21:13Z","timestamp":1259706073000},"page":"635-654","source":"Crossref","is-referenced-by-count":6,"title":["Estimating overnight\n                    <i>de facto<\/i>\n                    population by forecasting symptomatic variables: an integrated framework"],"prefix":"10.1002","volume":"29","author":[{"given":"Ricard","family":"Rigall\u2010I\u2010Torrent","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2009,12]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-306-47630-3_19"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0033-0124.2004.00445.x"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2070(89)90012-5"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.4135\/9781412986045"},{"key":"e_1_2_7_6_1","first-page":"56","article-title":"Poblaci\u00f3n flotante en los municipios catalanes 1998 [De facto population in Catalan jurisdictions 1998]","author":"Costa \u00c0","year":"2001","journal-title":"Revista Fuentes Estad\u00edsticas"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0160-7383(97)00025-X"},{"key":"e_1_2_7_8_1","first-page":"48","article-title":"Forecasting a Seasonal Population","volume":"23","author":"Friedman SK.","year":"1988","journal-title":"Business Economics"},{"issue":"1","key":"e_1_2_7_9_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3233\/JEM-1996-22102","article-title":"Measurement of Florida temporary residents using a telephone survey","volume":"22","author":"Galvez J","year":"1996","journal-title":"Journal of Economic and Social Measurement"},{"key":"e_1_2_7_10_1","unstructured":"Generalitat de Catalunya.1994.Departament de Medi Ambient.La Poblaci\u00f3 Estacional de Catalunya: Una Perspectiva Ambiental[Seasonal Population in Catalonia: An Environmental Perspective]. Generalitat de Catalunya Departament de Medi Ambient."},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.1077"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-2257.1976.tb00305.x"},{"key":"e_1_2_7_13_1","volume-title":"Econometric Analysis","author":"Greene WH."},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/0471725277"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118490082"},{"key":"e_1_2_7_16_1","volume-title":"Survey Methodology","author":"Groves RM","year":"2004"},{"key":"e_1_2_7_17_1","doi-asserted-by":"crossref","DOI":"10.1515\/9780691218632","volume-title":"Time Series Analysis","author":"Hamilton JD.","year":"1994"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1019560405310"},{"key":"e_1_2_7_19_1","volume-title":"A Guide to Econometrics","author":"Kennedy 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Forecasting"],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Ffor.1166","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.1166","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T21:49:53Z","timestamp":1696888193000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/for.1166"}},"issued":{"date-parts":[[2009,12]]},"references-count":38,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2010,11]]}},"alternative-id":["10.1002\/for.1166"],"URL":"https:\/\/doi.org\/10.1002\/for.1166","archive":["Portico"],"ISSN":["0277-6693","1099-131X"],"issn-type":[{"value":"0277-6693","type":"print"},{"value":"1099-131X","type":"electronic"}],"published":{"date-parts":[[2009,12]]}},{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:18:09Z","timestamp":1787167089818,"version":"build-2736575974"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2020,6,25]],"date-time":"2020-06-25T00:00:00Z","timestamp":1593043200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Forecasting"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Using receiver operating characteristic (ROC) techniques, we evaluate the predictive content of the monthly main economic indicators (MEI) of the Organization for Economic Co\u2010operation and Development (OECD) for predicting both growth cycle and business cycle recessions at different horizons. From a sample that covers 123 indicators for 32 OECD countries as well as for Brazil, China, India, Indonesia, the Russian Federation, and South Africa, our results suggest that the OECD's MEI show a high overall performance in providing early signals of economic downturns worldwide, albeit they perform a bit better at anticipating business cycles than growth cycles. Although the performance for OECD and non\u2010OECD members is similar in terms of timeliness, the indicators are more accurate at anticipating recessions for OECD members. Finally, we find that some single indicators, such as interest rates, spreads, and credit indicators, perform even better than the composite leading indicators.<\/jats:p>","DOI":"10.1002\/for.2709","type":"journal-article","created":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T01:32:20Z","timestamp":1590975140000},"page":"80-93","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Evaluating the OECD\u2019s main economic indicators at anticipating recessions*"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3158-5614","authenticated-orcid":false,"given":"M\u00e1ximo","family":"Camacho","sequence":"first","affiliation":[{"name":"Universidad de Murcia  Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gonzalo","family":"Palmieri","sequence":"additional","affiliation":[{"name":"Universidad de Murcia  Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,6,25]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1257\/mac.3.2.246"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.2345"},{"key":"e_1_2_6_4_1","volume-title":"Cyclical analysis of time series: Selected procedures and computer programs","author":"Bry G.","year":"1971"},{"key":"e_1_2_6_5_1","unstructured":"Burgstaller J.(2002).Are stock returns a leading indicator for real macroeconomic developments? 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We transformed each type of raw data into the possibility of victory as a forecasting model. Besides the four single forecasts, namely Facebook fans, Facebook \u201cpeople talking about this\u201d (PTAT) statistics, opinion polls, and prediction markets, we generated three combined forecasts by associating various combinations of the four components. Then, we examined the predictive performance of each forecast on vote shares and the elected\/non\u2010elected outcome across the election period. Our findings, based on the evidence of Taiwan's 2018 county and city elections, showed that incorporating the Facebook PTAT statistic with polls and prediction markets generates the most powerful forecast. Moreover, we recognized the matter of the time horizons where the best proposed model has better accuracy gains in prediction\u2014in the \u201clate of election,\u201d but not in \u201capproaching election\u201d. The patterns of the trend of accuracy across time for each forecasting model also differ from one another. We also highlighted the complementarity of various types of data in the paper because each forecast makes important contributions to forecasting elections.<\/jats:p>","DOI":"10.1002\/for.2711","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T14:57:17Z","timestamp":1591714637000},"page":"132-143","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A new insight into combining forecasts for elections: The role of social media"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4437-0201","authenticated-orcid":false,"given":"Chih\u2010Yu","family":"Chin","sequence":"first","affiliation":[{"name":"Department of Information Management Chung Yuan Christian University  Taoyuan, Taiwan, No. 200, Chung Pei Road, Zhongli District Taoyuan City 32023 Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8311-7778","authenticated-orcid":false,"given":"Cheng\u2010Lung","family":"Wang","sequence":"additional","affiliation":[{"name":"Big Data Co., Ltd  Taipei, Taiwan, 5F., No. 145, Sec. 2, Min Sheng E. 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B. &Gulati G. J. J.(2007 August).Social Networks in Political Campaigns: Facebook and the 2006 Midterm Elections. 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Using high\u2010frequency transaction data, trading intensity is measured by price duration and incorporated in the class of heterogeneous autoregressive (HAR) models. Results provide compelling evidence that trading intensity improves the forecasting performance of a highly competitive set of HAR models, commonly used in the literature. HAR extensions that incorporate price duration systematically deliver the lowest forecast errors and generate economically significant gains in volatility targeting exercise over multiple horizons. However, results show no evidence in favor of a unique duration\u2010augmented model. The predictive ability of price duration is supported by a number of robustness checks, including alternative estimation windows, bull and bear market states, and alternative thresholds that define price events.<\/jats:p>","DOI":"10.1002\/for.2989","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T10:30:12Z","timestamp":1684492212000},"page":"1909-1929","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Forecasting realized volatility of Bitcoin: The informative role of price duration"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6545-9435","authenticated-orcid":false,"given":"Skander","family":"Slim","sequence":"first","affiliation":[{"name":"Dubai Business School University of Dubai  Dubai UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Tabche","sequence":"additional","affiliation":[{"name":"Dubai Business School University of Dubai  Dubai UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yosra","family":"Koubaa","sequence":"additional","affiliation":[{"name":"LaREMFiQ \u2010 IHEC University of Sousse  Sousse Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Osman","sequence":"additional","affiliation":[{"name":"Dubai Business School University of Dubai  Dubai UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Karathanasopoulos","sequence":"additional","affiliation":[{"name":"Dubai Business School University of Dubai  Dubai UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2018.08.010"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/1.1.3"},{"key":"e_1_2_9_4_1","unstructured":"Andersen T. 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We find that, on average, (i) there is a positive forecast error (FE) in the debt\u2010to\u2010gross domestic product (GDP) projections\u2014that is, realized debt ratios are larger than forecasts; (ii) the FE increases with the projection horizon and is statistically significant and large\u2014about 10% of GDP at the 5\u2010year horizon; (iii) the magnitude is similar between advanced economies (AEs) and emerging markets and developing economies (EMDEs) and in EMDEs is present irrespective of recessions while for AEs is associated with surprise recessions in the forecast horizon; (iv) FEs are not statistically different between IMF program and non\u2010program cases; and (v) positive FEs are only partly attributable to optimism about growth or the fiscal balance. 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The class of linear and quantile predictive regression models is proposed for the analysis of real gross domestic product (GDP) growth, and a Bayesian approach for model selection is developed, by using a computationally flexible Markov chain Monte Carlo stochastic search algorithm that explores the posterior distribution of linear and quantile models, and identifies the relevant predictor variables. Penalized likelihood regression models are also implemented to tackle the issue of model selection. The model confidence set approach is applied and verifies that the selected models identified by the stochastic search algorithm belong to the set of superior models. 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These are the EEC's model QUEST as operated by the Deutsches Institut fur Wirtschaftsforschung (DIW); the GEM model jointly operated by the National Institute of Economic and Social Research and the London Business School; the Oxford Economic Forecasting model; the MIMOSA model used by the Observatoire Francais des Conjonctures Economiques (OFCE) and Centre D'Etudes Prospectives et D'Informations Internationales (CEPII), and OECD's Interlink model. The simulation experiments are designed to clarify the interdependencies and relative structures of the major European economies using both (internal) single\u2010country and external common shocks.<\/jats:p><jats:p>All the models tend to adopt similar specifications across the different countries but differences in simulation responses still occur and cross\u2010model differences often dominate those between countries. Several general qualitative conclusions can be drawn, however. For example, independent fiscal policy under a monetary policy of fixed real interest rates has only limited power to raise output, although full crowding out does not occur. Spillover effects to the other European economies are weak with respect to independent fiscal expansions but stronger for an external shock.<\/jats:p><jats:p>The model comparisons reveal differences in modelling strategies, notably with respect to the treatment of inflation on consumption, the role of activity on import behaviour and the influence of excess demand on wages. These; are all key factors in explaining simulation differences between the models. 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This paper studies the sources of forecast revisions. By decomposing the fixed\u2010event forecast error into a rational component due to unanticipated future shocks and an irrational component due to measurement error in acquired information or forecasters' reactions, we derive the conditions under which fixed\u2010event forecasts that contain an irrational forecast error can still possess the second moment properties of rational forecasts. We show that internal consistency of fixed\u2010event forecasts depends on the magnitude of the rational and irrational components in the revisions. As such, fixed\u2010event forecasts subject to irrational error may still be internally consistent, although they are not rational, as evidence in many empirical studies. We illustrate our methodology with the SPF inflation forecasts data. Our results show evidence of a sizeable and heterogeneous irrational forecast error component across forecast horizons and a high irrational\u2010to\u2010news ratio for most forecasters. This finding also provides insight into why forecast revision effort is not always fully compensated by revision reward.<\/jats:p>","DOI":"10.1002\/for.2871","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:07:25Z","timestamp":1653005245000},"page":"1338-1355","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Are internally consistent forecasts rational?"],"prefix":"10.1002","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0186-4412","authenticated-orcid":false,"given":"Jing","family":"Tian","sequence":"first","affiliation":[{"name":"Tasmanian School of Business and Economics University of Tasmania  Hobart Tasmania Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1876-0633","authenticated-orcid":false,"given":"Firmin","family":"Doko Tchatoka","sequence":"additional","affiliation":[{"name":"School of Economics and Public Policy University of Adelaide  Adelaide South Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Goodwin","sequence":"additional","affiliation":[{"name":"Hillwood Berries  Hillwood Tasmania Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,6]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1992.tb04010.x"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmoneco.2013.08.005"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","unstructured":"Bordalo P. 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We present regression and out\u2010of sample forecasting results demonstrating that information on the age composition of the Federal debt is useful for forecasting term premia. We show that the multiprocess mixture model, a multi\u2010state time\u2010varying parameter model, outperforms the commonly used GARCH model in out\u2010of\u2010sample forecasts of term premia. The results underscore the importance of modelling term premia, as a function of economic variables rather than just as a function of asset covariances as in the conditional heteroscedasticity models. Copyright \u00a9 2001 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/for.805","type":"journal-article","created":{"date-parts":[[2002,8,25]],"date-time":"2002-08-25T16:22:28Z","timestamp":1030292548000},"page":"519-539","source":"Crossref","is-referenced-by-count":3,"title":["Term premia and the maturity composition of the Federal debt: new evidence from the term structure of interest rates"],"prefix":"10.1002","volume":"20","author":[{"given":"Basma","family":"Bekdache","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2001,11,9]]},"reference":[{"volume-title":"Does Debt Management Matter?","year":"1992","author":"Agell J","key":"e_1_2_1_2_1"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.2307\/1391369"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-405X(87)90045-6"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1257\/jep.9.3.129"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-3932(94)90004-3"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1086\/260595"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.2307\/1991827"},{"issue":"4","key":"e_1_2_1_9_1","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1093\/oxfordjournals.oep.a042066","article-title":"Time\u2010varying risk perceptions and the pricing of risky assets","volume":"44","author":"Friedman BM","year":"1992","journal-title":"Oxford Economic Papers"},{"key":"e_1_2_1_10_1","first-page":"359","volume-title":"Bayesian Analysis of Time Series and Dynamic Models","author":"Gordon K","year":"1988"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.2307\/2289768"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(94)01632-A"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.2307\/2109549"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1889(88)90047-4"},{"key":"e_1_2_1_15_1","doi-asserted-by":"crossref","DOI":"10.1515\/9780691218632","volume-title":"Time Series Analysis","author":"Hamilton JD","year":"1994"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-3932(94)90003-5"},{"key":"e_1_2_1_17_1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/j.2517-6161.1976.tb01586.x","article-title":"Bayesian forecasting","volume":"38","author":"Harrison PJ","year":"1976","journal-title":"Journal of Royal Statistical Society Series B"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1086\/296144"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-3932(83)90064-8"},{"key":"e_1_2_1_20_1","unstructured":"McCullochJH KwonH.1993.U.S. term structure data 1947\u20131991. 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Techniques to incorporate constraints into neural networks (NN), such as neural ordinary differential equations (Neural ODEs), have been used. However, these introduce hyperparameters that require manual tuning through trial and error, raising doubts about the successful incorporation of constraints into the generated model. This paper describes in detail the two\u2010stage training method for Neural ODEs, a simple, effective, and penalty parameter\u2010free approach to model constrained systems. In this approach, the constrained optimization problem is rewritten as two optimization subproblems that are solved in two stages. The first stage aims at finding feasible NN parameters by minimizing a measure of constraints violation. The second stage aims to find the optimal NN parameters by minimizing the loss function while keeping inside the feasible region. We experimentally demonstrate that our method produces models that satisfy the constraints and also improves their predictive performance, thus ensuring compliance with critical system properties and also contributing to reducing data quantity requirements. Furthermore, we show that the proposed method improves the convergence to an optimal solution and improves the explainability of Neural ODE models. Our proposed two\u2010stage training method can be used with any NN architectures.<\/jats:p>","DOI":"10.1002\/for.3270","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T02:05:06Z","timestamp":1742781906000},"page":"1785-1805","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Two\u2010Stage Training Method for Modeling Constrained Systems With Neural Networks"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4502-937X","authenticated-orcid":false,"given":"C.","family":"Coelho","sequence":"first","affiliation":[{"name":"Centre of Mathematics (CMAT) University of Minho  Braga Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6235-286X","authenticated-orcid":false,"given":"M.\u00a0Fernanda\u00a0P.","family":"Costa","sequence":"additional","affiliation":[{"name":"Centre of Mathematics (CMAT) University of Minho  Braga Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5477-3226","authenticated-orcid":false,"given":"L.L.","family":"Ferr\u00e1s","sequence":"additional","affiliation":[{"name":"Centre of Mathematics (CMAT) University of Minho  Braga Portugal"},{"name":"Department of Mechanical Engineering (Section of Mathematics) and CEFT \u2010 FEUP University of Porto  Porto Portugal"},{"name":"ALiCE, Faculdade de Engenharia University of Porto  Porto Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,3,23]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/nme.947"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.camwa.2010.08.018"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04167-0"},{"key":"e_1_2_9_5_1","unstructured":"Coelho C. M. F. P.Costa andL. L.Ferr\u00e1s.2023. \u201cPrior Knowledge Meets Neural ODEs: A Two\u2010Stage Training Method for Improved Explainability.\u201d InThe First Tiny Papers Track at ICLR 2023 Tiny Papers @ ICLR 2023 Kigali Rwanda May 5 2023 edited byK.Maughan R.Liu andT. F.Burns.https:\/\/openreview.net\/pdf?id=p7sHcNt_tqo."},{"key":"e_1_2_9_6_1","unstructured":"Coelho C. M. F. P.Costa andL. L.Ferr\u00e1s.2023. \u201cSynthetic Chemical Reaction.\u201d Kaggle."},{"key":"e_1_2_9_7_1","unstructured":"Coelho C. M. F. P.Costa andL. L.Ferr\u00e1s.2023. \u201cWorld Population Growth.\u201d Kaggle."},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","unstructured":"Fioretto F. P.Van\u00a0Hentenryck TWKMak et\u00a0al.2021. \u201cLagrangian Duality for Constrained Deep Learning.\u201d InMachine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track: European Conference ECML PKDD 2020 Ghent Belgium September 14\u201318 2020 Proceedings Part V 118\u2013135. 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It is a well\u2010known market (auctions) microstructure fact that the daily wind forecasts are information available to the market before the daily auction bid deadline at 11\u2009a.m. The main objective is therefore to establish conditional and marginal step ahead spot price density forecast using a stochastic representation of the lagged, synchronously reported and stationary spot price and wind forecast movements. Using an upward expansion path applying the Schwarz (Bayesian information criterion [BIC]) criterion and a battery of residual test statistics, an optimal maximum likelihood process density is suggested. The optimal specification reports a significant negative covariance between the daily price and wind forecast movements. Conditional on bivariate lags from the<jats:italic>SNP<\/jats:italic>information and using the known market information for wind forecast movements at<jats:italic>t<\/jats:italic><jats:sub>1<\/jats:sub>, the paper establishes one\u2010step\u2010ahead bivariate and marginal day\u2010ahead spot price movement densities. The result shows that wind forecasts significantly influence the synchronously reported spot price densities (means and volatilities). The paper reports day\u2010ahead bivariate and marginal densities for spot price movements conditional on several very plausible price and wind forecast movements. The paper suggests day\u2010ahead spot price predictions from conditional and synchronously reported wind forecasts movements. 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This paper extends that line of research by utilizing intra\u2010day data and obtaining daily volatility forecasts from a range of models based upon the higher\u2010frequency data. The volatility forecasts are appraised using four different measures of \u2018true\u2019 volatility and further evaluated using regression tests of predictive power, forecast encompassing and forecast combination. Our results show that the daily GARCH(1,1) model is largely inferior to all other models, whereas the intra\u2010day unadjusted\u2010data GARCH(1,1) model generally provides superior forecasts compared to all other models. Hence, while it appears that a daily GARCH(1,1) model can be beaten in obtaining accurate daily volatility forecasts, an intra\u2010day GARCH(1,1) model cannot be. 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By using vintage data updated on a monthly basis, we compare their ability to date<jats:italic>ex post<\/jats:italic>the occurrence of turning points, evaluate the stability over time of the signal emitted by the models and assess their ability to detect in real\u2010time recession signals. We show that the competitive use of these models provides a more robust analysis and detection of turning points. To perform the complete analysis, we have built a historical vintage database for the euro area going back to 1970 for two monthly macroeconomic variables of major importance for short\u2010term economic outlook, namely the industrial production index and the unemployment rate. Copyright \u00a9 2013 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/for.2260","type":"journal-article","created":{"date-parts":[[2013,7,22]],"date-time":"2013-07-22T10:16:43Z","timestamp":1374488203000},"page":"577-586","source":"Crossref","is-referenced-by-count":21,"title":["Evaluation of Regime Switching Models for Real\u2010Time Business Cycle Analysis of the Euro Area"],"prefix":"10.1002","volume":"32","author":[{"given":"Monica","family":"Billio","sequence":"first","affiliation":[{"name":"Dipartimento di Economia Universit\u00e0 Ca\u2019 Foscari Venezia Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"Ferrara","sequence":"additional","affiliation":[{"name":"EconomiX UMR 7235 Banque de France and University Paris Ouest France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dominique","family":"Gu\u00e9gan","sequence":"additional","affiliation":[{"name":"CES UMR 8174, University Paris 1 Panth\u00e9on\u2010Sorbonne France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gian Luigi","family":"Mazzi","sequence":"additional","affiliation":[{"name":"Eurostat Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2013,7,22]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1974.1100705"},{"key":"e_1_2_7_3_1","volume-title":"Growth and Cycle in the Euro Zone","author":"Anas J","year":"2007"},{"key":"e_1_2_7_4_1","unstructured":"BillioM FerraraL Gu\u00e9ganD MazziJL.2009.Evaluation of nonlinear time\u2010series models for real\u2010time business cycle analysis of the Euro area. 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This decision was a surprise and did not end all the conflicting opinions expressed by economists. This matter was finally settled in July 2002 after a revision to the 2001 real gross domestic product showed negative growth rates for its first three quarters.\nA series of political and economic events in the years 2000\u201301 have increased the amount of uncertainty in the state of the economy, which in turn has resulted in the production of less reliable economic indicators and forecasts. This paper evaluates the performance of two very reliable methodologies for predicting a downturn in the US economy using composite leading economic indicators (CLI) for the years 2000\u201301. It explores the impact of the monetary policy on CLI and on the overall economy and shows how the gradualness and uncertainty of this impact on the overall economy have affected the forecasts of these methodologies. It suggests that the overexposure of the CLI to the monetary policy tools and a strong, but less effective, expansionary money policy have been the major factors in deteriorating the predictions of these methodologies. To improve these forecasts, it has explored the inclusion of the CLI diffusion index as a prior in the Bayesian methodology.\u2003Copyright \u00a9 2004 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/for.923","type":"journal-article","created":{"date-parts":[[2004,11,13]],"date-time":"2004-11-13T00:35:50Z","timestamp":1100306150000},"page":"463-477","source":"Crossref","is-referenced-by-count":10,"title":["Monetary policy, composite leading economic indicators and predicting the 2001 recession"],"prefix":"10.1002","volume":"23","author":[{"given":"Mehdi","family":"Mostaghimi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2004,10,29]]},"reference":[{"key":"e_1_2_1_2_1","unstructured":"DelNegro M.2001. 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It lowers waste and increases energy efficiency via the use of sophisticated forecasting and load optimization techniques. However, challenges such as data inconsistency processing and integration of diverse energy sources still need to be addressed. In this paper, an optimized IoT\u2010based intelligent energy management system for enhanced renewable generation through advanced forecasting and load strategies (IOT\u2010IEMS\u2010RGFLS\u2010GPTPINN) is proposed. Firstly, the input data is gathered from the National Solar Radiation Database. Then the input data is preprocessed using an adaptive higher\u2010order singular value decomposition clutter filter (AHOSVDC) for normalization. The preprocessed data is then fed into the prediction segment by using generative pretrained physics\u2010informed neural networks (GPT\u2010PINN) to predict the accurately forecast renewable energy generation and load demand. The battlefield optimization algorithm (BFOA) is used for enhancing GPT\u2010PINN parameters. The proposed IOT\u2010IEMS\u2010RGFLS\u2010GPTPINN technique is executed in Python. The proposed method's performance was evaluated using performance indicators such as mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The IOT\u2010IEMS\u2010RGFLS\u2010GPTPINN achieves an MAE of 0.011%, outperforming the other existing methods SAT\u2010IRES\u2010IOT for 0.052%, RA\u2010DSEM\u2010PSO for 0.034%, and EDSM\u2010SIMS\u2010LEO for 0.063%. This includes existing methods such as Enhanced demand\u2010side management for solar\u2010based isolated microgrid systems: Load prioritization and energy optimization (EDSM\u2010SIMS\u2010LEO), Recent advancement in demand\u2010side energy management systems for optimal energy utilization (RA\u2010DSEM\u2010PSO), and Smart agriculture technology: An integrated framework of renewable energy resources, IoT\u2010based energy management, and precision robotics (SAT\u2010IRES\u2010IOT).<\/jats:p>","DOI":"10.1002\/for.70157","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:11:25Z","timestamp":1780272685000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimized Internet of Things\u2010Based Intelligent Energy Management System for Enhanced Renewable Generation through Advanced Forecasting and Load Strategies"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8083-0656","authenticated-orcid":false,"given":"X.","family":"Mercilin Raajini","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G.","family":"Rajesh","sequence":"additional","affiliation":[{"name":"Department of Information Technology, MIT Campus Anna University  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,31]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","first-page":"23504","DOI":"10.1109\/ACCESS.2024.3358182","article-title":"Short\u2010Term Load Forecasting in Smart Grids Using Hybrid Deep Learning","volume":"12","author":"Asiri M. 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Based on data available to correspondents at the time of survey completion, we propose variables that might inform the confidence that can be attached to their predictions. Having calibrated the survey predictors' directional accuracy, we model the probability of a correct directional prediction using logistic regression with the proposed variables. For point forecasting, we compare the accuracy of rescaled survey forecasts with time series benchmarks and some survey\/time series hybrid models. In addition, using the same set of variables, we model the magnitude of survey prediction errors. Directional forecast tests showed that three out of four survey predictors have value but are biased and inefficient. For shorter horizons we found that survey forecasts, enhanced by time series data, significantly improved point forecasting accuracy. For longer horizons the survey predictors were at least as accurate as alternatives. The usefulness of the more accurate of the predictors examined is enhanced by auxiliary information, namely the probability of directional accuracy and the estimated error magnitude.<\/jats:p>","DOI":"10.1002\/for.2567","type":"journal-article","created":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T16:00:17Z","timestamp":1544198417000},"page":"236-255","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Enhancing survey\u2010based investment forecasts"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9297-806X","authenticated-orcid":false,"given":"Ciaran","family":"Driver","sequence":"first","affiliation":[{"name":"School of Finance and Management, SOAS University of London London UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6689-5052","authenticated-orcid":false,"given":"Nigel","family":"Meade","sequence":"additional","affiliation":[{"name":"The Business School Imperial College London London UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,1,15]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.2307\/3872476","article-title":"Interest rates effect on output: Evidence from a GDP forecasting model for South Africa","volume":"49","author":"Aron J.","year":"2002","journal-title":"IMF Staff Papers"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.5089\/9781451858976.001"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/1368-423X.00102"},{"key":"e_1_2_9_5_1","first-page":"395","article-title":"Individual responses to business tendency surveys and the forecasting of manufactured production: An assessment of the Mitchell, Smith and Weale dis\u2010aggregate indicators on French data","volume":"2006","author":"Biau O.","year":"2006","journal-title":"Economie et Statistique"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.1163"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2011.01.020"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2012.07.005"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmoneco.2009.02.001"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/00036840600690272"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2006.04.004"},{"volume-title":"Practical nonparametric statistics","year":"1999","author":"Conover W. 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The projection method used by the Department of the Environment is examined in the context of these criteria and it is concluded that it is both practical and robust. However, it is open to criticism, first because of its failure to make the best use of the available data and of theoretical knowledge, and secondly because of its \u2018black box\u2019 nature. An alternative two\u2010stage strategy is developed. The first stage involves constructing projections using a new curve\u2010fitting method which takes account of within cohort life\u2010cycle headship rate changes. The second is a method of analysing the resulting projections by modelling transition rates between different household states. 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It is shown how, in a Bayesian framework, a generalized version of the Hodrick\u2013Prescott filter is obtained by specifying prior densities on the signal\u2010to\u2010noise ratio (<jats:italic>q<\/jats:italic>) in the underlying unobserved components model. This helps ensure an appropriate degree of smoothness in the estimated trend while allowing for uncertainty in <jats:italic>q<\/jats:italic>. The article discusses the important issue of prior elicitation for time series recorded at different frequencies. By combining prior expectations with the likelihood, the Bayesian approach permits detrending in a way that is more consistent with the properties of the series. 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The mixture Gaussian distribution of the error can vary from time to time. The Bayesian Information Criterion and the EM algorithm are used to estimate the number of parameters as well as the model parameters and their standard errors. The new model is applied to the S&amp;P500 Index and Hang Seng Index and compared with GARCH models with Gaussian error and Student's<jats:italic>t<\/jats:italic>error. The result shows that the IGARCH effect in these index returns could be the result of the mixture of one stationary volatility component with another non\u2010stationary volatility component. 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Using data from 2003 to 2018, five different forecasting methods are used: ETS, ARIMA, STAR, a classical beta convergence based model, and a spatial beta convergence model. First, it is shown that overall municipal crime disparities are steadily decreasing over time. This indicates that convergence and spatial effects are pivotal for the study of the dynamics of crime in Colombian municipalities. Time series cross\u2010validation for 4\u2010year ahead forecasts is implemented to assess the accuracy of all models. It is found that the STAR and the beta models have the lowest root mean squared errors. Therefore, as time goes by, space appears to play a more important role in the evolution of homicide rates. 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We argue that the most cited theoretical arguments for the presence of long memory do not imply the fractional difference operator and assess the performance of the autoregressive fractionally integrated moving average (ARFIMA) model when forecasting series with long memory generated by nonfractional models. We find that ARFIMA models dominate in forecast performance regardless of the long memory generating mechanism and forecast horizon. Nonetheless, forecasting uncertainty at the shortest forecast horizon could make short memory models provide suitable forecast performance, particularly for smaller degrees of memory. Additionally, we analyze the forecasting performance of the heterogeneous autoregressive (HAR) model, which imposes restrictions on high\u2010order AR models. We find that the structure imposed by the HAR model produces better short and medium horizon forecasts than unconstrained AR models of the same order. 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This is done in a two\u2010step process where we first estimate the conditional predictive accuracy of each expert given a vector of covariates\u2014or pooling variables\u2014and then combine the predictive distributions of the experts conditional on this local predictive accuracy. To estimate the local predictive accuracy of each expert, we introduce the simple, fast, and interpretable\n                    <jats:italic>caliper method<\/jats:italic>\n                    . Expert pooling weights from the local prediction pool approaches the equal weight solution whenever there is little data on local predictive performance, making the pools robust and adaptive. We also propose a local version of the widely used optimal prediction pools. Local prediction pools are shown to outperform the widely used optimal linear pools in a macroeconomic forecasting evaluation and in predicting daily bike usage for a bike rental company.\n                  <\/jats:p>","DOI":"10.1002\/for.3030","type":"journal-article","created":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T03:20:25Z","timestamp":1694229625000},"page":"103-117","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Local prediction pools"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6929-1526","authenticated-orcid":false,"given":"Oscar","family":"Oelrich","sequence":"first","affiliation":[{"name":"Department of Statistics Stockholm University  Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2786-2519","authenticated-orcid":false,"given":"Mattias","family":"Villani","sequence":"additional","affiliation":[{"name":"Department of Statistics Stockholm University  Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4415-8734","authenticated-orcid":false,"given":"Sebastian","family":"Ankargren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"issue":"4","key":"e_1_2_8_2_1","first-page":"111","article-title":"Modern forecasting models in action: Improving macroeconomic analyses at central banks","volume":"3","author":"Adolfson M.","year":"2007","journal-title":"International Journal of Central Banking"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1057\/jors.1969.103"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177699597"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316870"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2013.04.009"},{"key":"e_1_2_8_7_1","article-title":"A flexible predictive density combination for large financial data sets in regular and crisis periods","author":"Casarin R.","year":"2023","journal-title":"Journal of Econometrics"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOAS285"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2070(89)90012-5"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2016.02.006"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1982.10477893"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-013-0040-3"},{"key":"e_1_2_8_13_1","volume-title":"Finite mixture and Markov switching models","author":"Fr\u00fchwirth\u2010Schnatter S.","year":"2006"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-013-9416-2"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177013825"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2011.02.017"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.2961"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2006.08.001"},{"key":"e_1_2_8_19_1","unstructured":"Johnson M. 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The investment horizon heterogeneity may induce the terrible data loss problem under traditional asset return definition, while in the real world, there are many portfolio makers whose investment horizons are not consistent. The methodological framework has three parts. First, this paper gives a novel multitimescale analysis (MTA) tool as a computation procedure to decompose the raw return series, and the decomposed subreturn series could represent the information for a portfolio maker with specific investment horizon and has the same data length as the raw return series. Second, proposed methodological framework uses a time\u2010varying parameter GAS\u2010D\u2010Vine\u2010Copula model to construct the joint distribution of subreturn series of multiassets in a portfolio. Third, due to the stochastic dominance consistency issue, this paper applies three different utility functions as the outputs of a portfolio strategy and two cost functions as the inputs of a portfolio strategy in an efficiency evaluation model. The empirical example of US aviation stock market data from 2013 to 2021 reveals that the Mean\u2010Skewness\u2010Volatility\u2010HMCR\u2010LPM multiobjective has the greatest numbers of optimal strategy timings for portfolio makers with 2\u2010, 3\u20135\u2010, and 10\u201350\u2010day\u2010length investment horizons. The investment horizon of 1\u2010day length is the least efficient, and the investment horizon between 10\u2010 and 50\u2010day length is the most efficient. 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However, little systematic research exists on the impact of media reports on corporate fraud detection; thus, our understanding of the impact is limited. Therefore, we are committed to determining how the configuration of different media report content systematically detects corporate fraud by logistical regression, grounded theory and qualitative comparative analysis (QCA). First, the media reports are classified into three major categories and 35 subclasses to determine their features through fraud triangle theory and grounded theory. Then, based on a dataset of 110 fraudulent listed companies and 110 matched listed companies from 2010 to 2020, three major features comprising 10 subclasses are identified by the logistical regression method. The causal configurations of the features of media reports that detect corporate fraud are explored using the QCA method. The results show that five particular associations can interpret corporate fraud revelation by meeting the equifinality and asymmetric causality principles. Finally, the combined model is proposed. Through 56 fraudulent listed companies and 56 matched listed companies from 2021 to 2022, the combined model is proven to be most effective in detecting corporate fraud. In summary, we offer theoretical contributions to corporate fraud detection and empirical experiences for corporate managers and regulators.<\/jats:p>","DOI":"10.1002\/for.3022","type":"journal-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T10:56:40Z","timestamp":1694084200000},"page":"58-80","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["How to detect and forecast corporate fraud by media reports? 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The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large\u2010cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles.<\/jats:p>","DOI":"10.1002\/for.2585","type":"journal-article","created":{"date-parts":[[2019,3,21]],"date-time":"2019-03-21T17:29:01Z","timestamp":1553189341000},"page":"600-619","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":185,"title":["An ensemble of LSTM neural networks for high\u2010frequency stock market classification"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6585-999X","authenticated-orcid":false,"given":"Svetlana","family":"Borovkova","sequence":"first","affiliation":[{"name":"School of Business and Economics Vrije Universiteit Amsterdam  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis","family":"Tsiamas","sequence":"additional","affiliation":[{"name":"School of Business and Economics Vrije Universiteit Amsterdam  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,5,10]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/13518479500000023"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0180944"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.2307\/2325486"},{"key":"e_1_2_7_7_1","unstructured":"Fischer T. &Krauss C.(2017).Deep learning with long short\u2010term memory networks for financial market predictions. 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(Lecture 6).Toronto Canada: University of Toronto."},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2004.03.016"},{"key":"e_1_2_7_13_1","unstructured":"Kennedy J. &Eberhart R.(1995).Particle swarm optimization In.Proceedings IEEE International Conference on Neural Networks.Piscataway NJ:IEEE Vol.\u00a04 pp.1942\u20131948."},{"key":"e_1_2_7_14_1","first-page":"160","volume-title":"Proceedings of the Sixth International Workshop on Machine Learning","author":"Kent A.","year":"1989"},{"key":"e_1_2_7_15_1","unstructured":"Lei Ba J. Kiros J. R. &Hinton G. E.(2016).Layer normalization. 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of Forecasting"],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this article, we propose a regression model for sparse high\u2010dimensional data from aggregated store\u2010level sales data. The modeling procedure includes two sub\u2010models of topic model and hierarchical factor regressions. These are applied in sequence to accommodate high dimensionality and sparseness and facilitate managerial interpretation.<\/jats:p><jats:p>First, the topic model is applied to aggregated data to decompose the daily aggregated sales volume of a product into sub\u2010sales for several topics by allocating each unit sale (\u201cword\u201d in text analysis) in a day (\u201cdocument\u201d) into a topic based on joint\u2010purchase information. This stage reduces the dimensionality of data inside topics because the topic distribution is nonuniform and product sales are mostly allocated into smaller numbers of topics. Next, the market response regression model for the topic is estimated from information about items in the same topic. The hierarchical factor regression model we introduce, based on canonical correlation analysis for original high\u2010dimensional sample spaces, further reduces the dimensionality within topics. Feature selection is then performed on the basis of the credible interval of the parameters' posterior density.<\/jats:p><jats:p>Empirical results show that (i) our model allows managerial implications from topic\u2010wise market responses according to the particular context, and (ii) it performs better than do conventional category regressions in both in\u2010sample and out\u2010of\u2010sample forecasts.<\/jats:p>","DOI":"10.1002\/for.2574","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T06:44:41Z","timestamp":1547793881000},"page":"440-458","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Measuring large\u2010scale market responses and forecasting aggregated sales: Regression for sparse high\u2010dimensional data"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4868-0140","authenticated-orcid":false,"given":"Nobuhiko","family":"Terui","sequence":"first","affiliation":[{"name":"Graduate School of Economics and Management Tohoku University  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9335-9802","authenticated-orcid":false,"given":"Yinxing","family":"Li","sequence":"additional","affiliation":[{"name":"Graduate School of Economics and Management Tohoku University  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,2,22]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2133806.2133826"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1162\/jmlr.2003.3.4-5.993"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2014.904232"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214503000000387"},{"key":"e_1_2_7_6_1","doi-asserted-by":"crossref","unstructured":"Griffiths T. L. &Steyvers M.(2004).Finding scientific topics. Proceedings of the National Academy of Science 101 5228\u20135235.","DOI":"10.1073\/pnas.0307752101"},{"key":"e_1_2_7_7_1","volume-title":"Market Response Models: Econometric and Time Series Analysis","author":"Hanssen D. 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Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. 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Operational forecasts of three weather elements are considered: (1) probability forecasts of precipitation occurrence, (2) categorical (i.e. non\u2010probabilistic) forecasts of maximum and minimum temperatures and (3) categorical forecasts of cloud amount. The objective forecasts are prepared by numerical\u2010statistical procedures, whereas the subjective forecasts are based on the judgements of individual forecasters. In formulating the latter, the forecasters consult information from a variety of sources, including the objective forecasts themselves. The precipitation probability forecasts are found to be both reliable and skilful, and evaluation of the temperature\/cloud amount forecasts reveals that they are quite accurate\/skilful. Comparison of the objective and subjective forecasts of precipitation occurrence indicates that the latter are generally more skilful than the former for shorter lead times (e.g. 12\u201324 hours), whereas the two types of forecasts are of approximately equal skill for longer lead times (e.g. 36\u201348 hours). Similar results are obtained for the maximum and minimum temperature forecasts. Objective cloud amount forecasts are more skilful than subjective cloud amount forecasts for all lead times. Examination of trends in performance over the last decade reveals that both types of forecasts for all three elements increased in skill (or accuracy) over the period, with improvements in objective forecasts equalling or exceeding improvements in subjective forecasts. The role and impact of the objective forecasts in the subjective weather forecasting process are discussed in some detail. The need to conduct controlled experiments and other studies of this process, with particular reference to the assimilation of information from different sources, is emphasized. Important characteristics of the forecasting system in meteorology are identified, and they are used to describe similarities and differences between weather forecasting and forecasting in other fields. Acquisition of some of these characteristics may be beneficial to other forecasting systems.<\/jats:p>","DOI":"10.1002\/for.3980030402","type":"journal-article","created":{"date-parts":[[2007,7,8]],"date-time":"2007-07-08T00:41:55Z","timestamp":1183855315000},"page":"369-393","source":"Crossref","is-referenced-by-count":45,"title":["A comparative evaluation of objective and subjective weather forecasts in the united states"],"prefix":"10.1002","volume":"3","author":[{"given":"Allan H.","family":"Murphy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara G.","family":"Brown","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2006,9,21]]},"reference":[{"key":"e_1_2_1_2_1","first-page":"1","volume-title":"Preprints of the Ninth Conference on Weather Forecasting and Analysis","author":"Allen G.","year":"1982"},{"key":"e_1_2_1_3_1","first-page":"7","volume-title":"Preprints of the Ninth Conference on Weather Forecasting and Analysis","author":"Belville J. 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We use a battery of econometric specifications to evaluate whether optimal currency portfolios implied by trading strategies based on exchange rate forecasts outperform single currencies and the equally weighted portfolio. We assess the differences in profitability of optimal currency portfolios for different types of investor preferences, two trading strategies, mean squared error\u2010based composite forecasts, and different forecast horizons. Our results indicate that there are clear benefits of integrating exchange rate forecasts from state\u2010of\u2010the\u2010art econometric models in currency portfolios. 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Based on different combinations of the short\u2010 and long\u2010term effects caused by extreme events, we extend the standard GARCH\u2010MIDAS model to characterize the different responses of the stock market for short\u2010 and long\u2010term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in\u2010sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH\u2010MIDAS\u2010SLES model by including the short\u2010 and long\u2010term impacts of extreme shocks for all forecasting horizons. 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The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not require us to fix the delay or the threshold parameters in advance. By adopting the Markov chain Monte Carlo techniques, we can identify the best subset model from a very large of number of possible models, and at the same time estimate the unknown parameters. A simulation experiment shows that the method is very effective. In its application to the US unemployment rate, the stochastic search method successfully selects lag one as the time delay and five best models from more than 4000 choices. Copyright \u00a9 2003 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/for.859","type":"journal-article","created":{"date-parts":[[2003,3,26]],"date-time":"2003-03-26T14:58:38Z","timestamp":1048690718000},"page":"49-66","source":"Crossref","is-referenced-by-count":22,"title":["Subset threshold autoregression"],"prefix":"10.1002","volume":"22","author":[{"given":"Mike K. 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S.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2003,1,17]]},"reference":[{"key":"e_1_2_1_2_1","volume-title":"Bayesian Inference in Statistical Analysis","author":"Box GEP","year":"1973"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.2307\/2347570"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610929208830987"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1099-131X(199912)18:7<505::AID-FOR728>3.0.CO;2-U"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.1995.tb00248.x"},{"key":"e_1_2_1_7_1","volume-title":"Optimal Statistical Decisions","author":"DeGroot M","year":"1970"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1993.10476353"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.1993.tb00156.x"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1993.10476364"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.1994.tb00188.x"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.1994.tb00208.x"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.1699114"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.1990.tb00043.x"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176325750"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-009-9941-1_24"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4684-7888-4"},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1111\/j.2517-6161.1980.tb01126.x","article-title":"Threshold autoregression, limit cycles and cyclical data (with discussion)","volume":"42","author":"Tong H","year":"1980","journal-title":"Journal of the Royal Statistical Society B"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1989.10478760"}],"container-title":["Journal of Forecasting"],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Ffor.859","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.859","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T22:18:29Z","timestamp":1733955509000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/for.859"}},"issued":{"date-parts":[[2003,1]]},"references-count":18,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2003,1]]}},"alternative-id":["10.1002\/for.859"],"URL":"https:\/\/doi.org\/10.1002\/for.859","archive":["Portico"],"ISSN":["0277-6693","1099-131X"],"issn-type":[{"value":"0277-6693","type":"print"},{"value":"1099-131X","type":"electronic"}],"published":{"date-parts":[[2003,1]]}},{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:43:13Z","timestamp":1786977793997,"version":"build-2736575974"},"reference-count":35,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Forecasting"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Aggregated long and short trading risk positions of speculative assets over time are likely to be unequal. This may be because of irrational decisions of traders and investors as well as catastrophic events that lead to pronounce or salient market crashes. Returns of such assets are therefore more likely to have one polynomial tail and one exponential tail. The generalized hyperbolic (GH) skewed Student\u2010\n                    <jats:italic>t<\/jats:italic>\n                    distribution is known to handle such situations quite well. In this paper, we use generalized autoregressive conditional heteroscedasticity (GARCH) models to empirically show the superiority of the GH skewed Student\u2010\n                    <jats:italic>t<\/jats:italic>\n                    distribution in forecasting the extreme tail risks of cryptocurrency returns in the presence of substantial skewness in comparison with some competing distributions. Furthermore, we show the practical significance of the GH skewed Student\u2010\n                    <jats:italic>t<\/jats:italic>\n                    distribution\u2010based risk forecasts in computing daily capital requirements. Evidence from the study suggests that the GH skewed Student\u2010\n                    <jats:italic>t<\/jats:italic>\n                    distribution model tends to be superior in forecasting volatility and expected shortfall (ES) but not value\u2010at\u2010risk. In addition, the distribution yields higher value\u2010at\u2010risk (VaR) exceptions but surprisingly avoids the red zone of the Basel II accord penalty zones and produces lower but optimal daily capital requirements. Therefore, in the presence of substantially skewed returns having exponential\u2010polynomial tails, we recommend the use of the GH skewed Student\u2010\n                    <jats:italic>t<\/jats:italic>\n                    distribution for parametric GARCH models in forecasting extreme tail risk.\n                  <\/jats:p>","DOI":"10.1002\/for.3154","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T03:56:07Z","timestamp":1715745367000},"page":"2731-2748","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Forecasting tail risk of skewed financial returns having exponential\u2010polynomial tails"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3571-4850","authenticated-orcid":false,"given":"Albert","family":"Antwi","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences, Faculty of Natural and Applied Sciences Sol Plaatje University  Kimberley Northern Cape 8300 South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuel N.","family":"Gyamfi","sequence":"additional","affiliation":[{"name":"Department of Accounting and Finance, Business School Ghana Institute of Public Administration  Accra Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anokye M.","family":"Adam","sequence":"additional","affiliation":[{"name":"Department of Finance University of Cape Coast  Cape Coast Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbj006"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3027631"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00391"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-009-8549-0_2"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9469.00045"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbaa013"},{"key":"e_1_2_11_8_1","unstructured":"Bernardi M. &Catania L.(2014 October 30).The Model Confidence Set package for R. arXiv.Org.https:\/\/arxiv.org\/abs\/1410.8504v1"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(86)90063-1"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.18576\/jsap\/110103"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.2307\/2527341"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/713665670"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.2307\/2669632"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1993.tb05128.x"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2003.10.003"},{"key":"e_1_2_11_16_1","first-page":"3","volume-title":"Tail behaviour and tail dependence of generalized hyperbolic distributions","author":"Hammerstein E.","year":"2016"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.800"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA5771"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.19030\/iber.v13i2.8447"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00378"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.4102\/sajems.v18i4.966"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.qref.2018.01.002"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.12.022"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1086\/294632"},{"key":"e_1_2_11_25_1","article-title":"3. Assessing volatility forecasting models: Why GARCH models take the lead","volume":"24","author":"Matei M.","year":"2009","journal-title":"Romanian Journal of Economic Forecasting"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.1054"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0927-5398(00)00012-8"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1515\/snde-2012-0021"},{"key":"e_1_2_11_29_1","unstructured":"Prause K.(1999).The Generalized Hyperbolic Model: Estimation Financial Derivatives and Risk Measures."},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-658-11908-9"},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-020-00217-x"},{"key":"e_1_2_11_32_1","unstructured":"Szego G.(2004).Risk Measures for the 21st Century.The Wiley Finance Series.https:\/\/www.wiley.com\/en-us\/Risk+Measures+for+the+21st+Century-p-9780470861547"},{"key":"e_1_2_11_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2015.07.005"},{"key":"e_1_2_11_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/9780470644560"},{"key":"e_1_2_11_35_1","unstructured":"Wold H.(1954).A Study in the Analysis of Stationary Time Series(Second revised edition)."},{"key":"e_1_2_11_36_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610926.2021.1990952"}],"container-title":["Journal of Forecasting"],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.3154","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T00:37:18Z","timestamp":1727829438000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/for.3154"}},"issued":{"date-parts":[[2024,5,15]]},"references-count":35,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10.1002\/for.3154"],"URL":"https:\/\/doi.org\/10.1002\/for.3154","archive":["Portico"],"ISSN":["0277-6693","1099-131X"],"issn-type":[{"value":"0277-6693","type":"print"},{"value":"1099-131X","type":"electronic"}],"published":{"date-parts":[[2024,5,15]]},"assertion":[{"value":"2023-04-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]},{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:42:40Z","timestamp":1786977760131,"version":"build-2736575974"},"reference-count":33,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2011,5,26]],"date-time":"2011-05-26T00:00:00Z","timestamp":1306368000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Forecasting"],"published-print":{"date-parts":[[2012,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Value\u2010at\u2010risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models is compared, including standard, threshold nonlinear and Markov switching generalized autoregressive conditional heteroskedasticity (GARCH) specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student\u2010\n                    <jats:italic>t<\/jats:italic>\n                    , skewed\u2010\n                    <jats:italic>t<\/jats:italic>\n                    and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia\u2013Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models outperformed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre crisis, while at the 1% level during and post crisis, for a 1\u2010day horizon, models with skewed\u2010\n                    <jats:italic>t<\/jats:italic>\n                    errors ranked best, while integrated GARCH models were favoured at the 5% level; (iii) all models forecast VaR less accurately and anti\u2010conservatively post crisis. Copyright \u00a9 2011 John Wiley &amp; Sons, Ltd.\n                  <\/jats:p>","DOI":"10.1002\/for.1237","type":"journal-article","created":{"date-parts":[[2011,5,26]],"date-time":"2011-05-26T22:47:20Z","timestamp":1306450040000},"page":"661-687","source":"Crossref","is-referenced-by-count":47,"title":["Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis"],"prefix":"10.1002","volume":"31","author":[{"given":"Cathy W.S.","family":"Chen","sequence":"first","affiliation":[{"name":"Feng Chia University Taichung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Gerlach","sequence":"additional","affiliation":[{"name":"University of Sydney Business School Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward\u2009M.\u2009H.","family":"Lin","sequence":"additional","affiliation":[{"name":"Feng Chia University Taichung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"W. C. W.","family":"Lee","sequence":"additional","affiliation":[{"name":"Feng Chia University Taichung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2011,5,26]]},"reference":[{"key":"e_1_2_10_2_1","volume-title":"Supervisory Framework for the Use of \u2018Backtesting\u2019 in Conjunction With the Internal Models Approach to Market Risk Capital Requirements","author":"Basel Committee on Banking Supervision","year":"1996"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(86)90063-1"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(92)90064-X"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.1995.tb00248.x"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2005.08.001"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-842X.2007.00498.x"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.1119"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.2307\/2527341"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.2307\/1912773"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/07474938608800095"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1198\/073500104000000370"},{"key":"e_1_2_10_13_1","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1093\/oso\/9780198523567.003.0038","volume-title":"Bayesian Statistics 5","author":"Gelman A","year":"1996"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1198\/jbes.2010.08203"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1993.tb05128.x"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-405X(96)00875-6"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.2307\/2527081"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/57.1.97"},{"key":"e_1_2_10_19_1","volume-title":"Value at Risk: The New Benchmark for Controlling Market Risk","author":"Jorion P","year":"1997"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.21314\/JOR.2002.069"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.3905\/jod.1995.407942"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.1049"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.1054"},{"key":"e_1_2_10_24_1","volume-title":"Has the Basel II Accord encouraged risk management during the 2008\u2009\u2013\u200909 financial crisis?","author":"McAleer M","year":"2009"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.1699114"},{"key":"e_1_2_10_26_1","volume-title":"J. P. Morgan Technical Document","author":"Morgan JP","year":"1996"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.2307\/2938260"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1257\/jel.41.2.478"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.840"},{"key":"e_1_2_10_30_1","first-page":"203","volume-title":"Time Series Analysis: Theory and Practice","author":"Taylor SJ","year":"1982"},{"key":"e_1_2_10_31_1","volume-title":"Modelling Financial Time Series","author":"Taylor SJ","year":"1986"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/0471746193"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.2307\/1392556"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1889(94)90039-6"}],"container-title":["Journal of Forecasting"],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Ffor.1237","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.1237","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,7]],"date-time":"2024-04-07T01:36:27Z","timestamp":1712453787000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/for.1237"}},"issued":{"date-parts":[[2011,5,26]]},"references-count":33,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2012,12]]}},"alternative-id":["10.1002\/for.1237"],"URL":"https:\/\/doi.org\/10.1002\/for.1237","archive":["Portico"],"ISSN":["0277-6693","1099-131X"],"issn-type":[{"value":"0277-6693","type":"print"},{"value":"1099-131X","type":"electronic"}],"published":{"date-parts":[[2011,5,26]]}},{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:42:40Z","timestamp":1786977760163,"version":"build-2736575974"},"reference-count":47,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T00:00:00Z","timestamp":1715299200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Forecasting"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The endless adverse effects of air pollution incidents have raised significant public concerns in the past few decades. The measure of air pollution, that is, the air quality index (AQI), is highly volatile and associated with different kinds of uncertainties. Following this, the study and development of accurate fuzzy time series forecasting (TSF) methods for predicting the AQI have a significant role in air pollution control and management. Motivated by this, in this paper, a systematic study is made to evaluate the true potential of fuzzy TSF methods employing traditional fuzzy set (TFS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), and neutrosophic fuzzy set (NFS) in forecasting the AQI. Two novel high\u2010order fuzzy TSF methods, TFS\u2010multilayer perceptron (MLP) and HFS\u2010MLP, are proposed employing TFS and HFS in which ratio trend variation of AQI data is used instead of original AQI, MLP is used to model the fuzzy logical relationships (FLRs), and none\/mean of aggregated membership values are used while modeling the FLRs using MLP. The results from the proposed fuzzy TSF methods are compared with recently proposed fuzzy TSF methods employing TFS, IFS, and NFS and six popular machine learning models, including MLP, support vector regression (SVR), Bagging Regressors, XGBoost, long\u2010short term memory (LSTM), and convolutional neural network (CNN). The \u201cWilcoxon Signed\u2010Rank test\u201d and \u201cFriedman and Nemenyi hypothesis test\u201d are applied to the results obtained by employing different ratios in the train\u2010validation\u2010test to draw decisive conclusions reliably. The simulation results show the statistical dominance of the proposed TFS\u2010MLP method over all other crisp and fuzzy TSF methods employed in this paper.<\/jats:p>","DOI":"10.1002\/for.3153","type":"journal-article","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T02:29:05Z","timestamp":1715394545000},"page":"2635-2658","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A study and development of high\u2010order fuzzy time series forecasting methods for air quality index forecasting"],"prefix":"10.1002","volume":"43","author":[{"given":"Sushree Subhaprada","family":"Pradhan","sequence":"first","affiliation":[{"name":"Department of Computer Science Engineering and Application Sambalpur University Institute of Information Technology  Sambalpur India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1409-3856","authenticated-orcid":false,"given":"Sibarama","family":"Panigrahi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering National Institute of Technology  Rourkela India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,5,10]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.05.039"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.04.001"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matcom.2010.09.011"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0969-6989(00)00011-4"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/15353702221079620"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(94)90229-1"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.07.044"},{"key":"e_1_2_8_9_1","volume-title":"Several similarity measures of neutrosophic sets","author":"Broumi S.","year":"2013"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8049504"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2013.09.025"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/019697202753306479"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.06.020"},{"key":"e_1_2_8_14_1","volume-title":"Minnesota pollution control agency","author":"County C.","year":"2003"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2008.07.020"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.05.040"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.02.057"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/01969722.2014.904135"},{"key":"e_1_2_8_19_1","volume-title":"The application of neural networks to production process control","author":"Hamburg J. 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We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models. The results show that the ML and regularization methods are efficient, even when there are only three predictors: daily, weekly, and monthly realized volatility (RV) lags. In addition, when the ML and regularization methods are applied, the results become more pronounced over longer forecasting horizons, as well as for weekly and monthly horizons. These ML methods are effective in approximating long\u2010term realized volatility. 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