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Both univariate (jointly with business conditions) and multivariate models are employed, whereas out\u2010of\u2010sample forecasts are generated, and the results are compared based on popular forecasting performance criteria. These criteria show that in the case of univariate models, the largest forecasting gains are obtained when the modelling process follows the kitchen sink autoregressive of order one (KS\u2010AR[1]) model with the business cycles being measured as the coincident indicator. In the case of multivariate models, the largest forecasting gains occur with the standard vector autoregressive (VAR) model for very short forecasting horizons and with the Bayesian VAR for longer horizons. The results are robust to both total and individual destinations. 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This study produces evidence that monetary policy transparency and communication policy of the Bank of England have information content in reducing disagreement about interest rate forecasts. Different from most extant studies employing the transparency index derived from official documents of the central banks, this study extends the literature by using a recently developed market\u2010based monetary transparency index. Moreover, this study analyzes forecast disagreement in a multivariate perspective based on survey data of short\u2010 and long\u2010term rates over short and long horizons. This study characterizes several patterns on forecast disagreement related to maturities of interest rates, forecast horizons, recessions, forward guidance, credibility, transparency, and communication policy. 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The proposed approach is based on a two\u2010step estimation procedure. The first step involves the combination of value\u2010at\u2010risk (VaR) forecasts at a grid of quantile levels. A range of parametric and semiparametric models is selected as the model universe in the forecast combination procedure. The quantile forecast combination weights are estimated by optimizing the quantile loss. In the second step, the expected shortfall (ES) is computed as a weighted average of combined quantiles. The quantiles weighting structure for ES forecasting is determined by minimizing a strictly consistent joint VaR and ES loss function of the Fissler\u2013Ziegel class. The proposed framework is applied to six stock market indices and its forecasting performance is compared to each individual model in the universe, a simple average approach and a weighted quantile approach. The forecasting results support the proposed framework.<\/jats:p>","DOI":"10.1002\/for.2972","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T05:39:00Z","timestamp":1676439540000},"page":"1648-1663","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4380-4925","authenticated-orcid":false,"given":"Giuseppe","family":"Storti","sequence":"first","affiliation":[{"name":"Department of Economics and Statistics University of Salerno Fisciano Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5693-9971","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Discipline of Business Analytics The University 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(2) The evaluation criterions RMSE, MAE, and MAPE of the ICEEMDAN\u2010CNN\u2010LSTM model are relatively small, with values of 0.0156, 0.0112, and 0.1679, respectively, indicating that the predictive performance of the model is optimal. (3) This paper has conducted various robust tests, all of which indicate that the proposed model has high prediction accuracy and stability. 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Existing approaches face two principal challenges: the inefficiency of conventional models under finite samples and the tendency of single machine learning models toward overfitting or underfitting. This paper introduces a nonlinear expectile regression model based on a blending ensemble framework, integrating neural networks, support vector machines, XGBoost, LightGBM, and random forests as base learners, with an expectile regression forest as the metalearner. Monte Carlo simulations confirm the method's robustness in finite samples. Applied to Chinese stock indices, the model outperforms both traditional linear specifications and each individual machine learning model in EVaR forecasting. Performance gains are statistically significant under stochastic volatility and TGARCH settings, as verified by Diebold\u2013Mariano and Giacomini\u2013White tests. SHAP analysis further shows that XGBoost and LightGBM contribute most to prediction, enhancing interpretability and offering insight into ensemble decision mechanisms.<\/jats:p>","DOI":"10.1002\/for.70154","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T02:01:11Z","timestamp":1776132071000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Financial Tail Risk Forecasting: A Blending Ensemble Framework for Nonlinear Expectile Regression"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3808-8154","authenticated-orcid":false,"given":"Yaolan","family":"Ma","sequence":"first","affiliation":[{"name":"School of Mathematics and Information Sciences North Minzu University Yinchuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingying","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Information Sciences North Minzu University Yinchuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/1351847X.2015.1052150"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304\u20104076(86)90063\u20101"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13087"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.3969\/j.issn.1672\u20105956.2022.05.010"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.12012\/CJoE2022\u20100047"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s001810000062"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbad014"},{"key":"e_1_2_11_9_1","volume-title":"Economics Books","author":"Diebold F. 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We adopt a regime\u2010switching framework in which sets of scenarios (\u201cviews\u201d) are used as Bayesian priors on economic regimes. Predictive densities coming from different views are then combined by optimizing objective functions of density forecasting. We illustrate the approach with an empirical application to quarterly real\u2010time forecasts of the US GDP growth rate, in which we exploit the Fed's macroeconomic scenarios used for bank stress tests. We show that the approach achieves good accuracy in terms of average predictive scores and good calibration of forecast distributions. 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However, due to high randomness of spot markets and many uncertainties in production environments, it is hard to forecast the products accurately. In this article, a hybrid model combining seasonal autoregressive integrated moving average (SARIMA) and least square support vector machines (LSSVMs) is proposed to forecast the potential demand of steel products. First, the SARIMA based on a multiobjective differential evolution with improved mutation strategies is developed to extract linear components of the potential demand. Then, a sparse strategy is designed to extract useful data and hence reduce computation complexity without loss of accuracy. Next, the LSSVMs combined with a single\u2010objective differential evolution are adopted to extract nonlinear components of the potential demand. 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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. In addition, they outperform the LSTM and 1\u2010D CNN baseline models when using the time series data representing historical stock prices. Furthermore, three ensemble deep learning models are constructed for performance comparison, including VGG16\u2010LSTM, 2\u2010D CNN\u2010LSTM, and the stacking model, in which the VGG16\u2010LSTM and the stacking models perform the best for the prediction of short\u2010 to mid\u2010term and mid\u2010 to long\u2010term stock prices, respectively.<\/jats:p>","DOI":"10.1002\/for.70099","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T23:43:40Z","timestamp":1767743020000},"page":"1350-1367","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Image\u2010Based Deep Learning Models for Stock Predictions: Combining Line, Candlestick, and Bar Charts"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5803-513X","authenticated-orcid":false,"given":"Wei\u2010Chao","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Information Management Chang Gung University Taoyuan Taiwan"},{"name":"Department of Digital Financial Technology Chang Gung University Taoyuan Taiwan"},{"name":"Department of Emergency Medicine Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming\u2010Chang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Business Administration National Chung Cheng University Chiayi Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5991-2253","authenticated-orcid":false,"given":"Chih\u2010Fong","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Information Management National Central University Taoyuan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jui\u2010Pin","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Information Management National Central University Taoyuan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-01892-5"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.07.019"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.11.008"},{"issue":"4","key":"e_1_2_10_5_1","first-page":"26","article-title":"Technical Analysis in Select Stocks of Indian Companies","volume":"2","author":"Boobalan C.","year":"2014","journal-title":"International Journal of Business and Administration Research Review"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113464"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-020-00187-0"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.01.079"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.12.068"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/13504850600993598"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/jrfm7010001"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.18178\/ijmlc.2017.7.5.632"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2017.11.054"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2019.08.001"},{"key":"e_1_2_10_15_1","volume-title":"Technical Analysis Power Tools for Active Investors","author":"Gerald A.","year":"2005"},{"key":"e_1_2_10_16_1","volume-title":"Candlestick Charting Explained: Timeless Techniques for Trading Stocks and Futures","author":"Gregory M. 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J.Yan J.Yan et\u00a0al.2024. \u201cA Stock Price Prediction Approach Based on Time Series Decomposition and Multi\u2010Scale CNN Using OHLCT Images.\u201darXiv:2410.19291."},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.3390\/info12100388"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.06.014"},{"key":"e_1_2_10_38_1","first-page":"921","article-title":"An Interpretable Neuro\u2010Fuzzy Approach to Stock Price Forecasting","volume":"23","author":"Rajab S.","year":"2019","journal-title":"Soft Computing"},{"key":"e_1_2_10_39_1","doi-asserted-by":"publisher","DOI":"10.1108\/978-1-78635-634-520161005"},{"key":"e_1_2_10_40_1","volume-title":"A Complete Guide to the Futures Market","author":"Schwager J. 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We start with the well\u2010known normal dynamic linear model, also known as the normal linear state space model, for which sequential state learning is available in closed form via standard Kalman filter and Kalman smoother recursions. Particle filters are then introduced as a set of Monte Carlo schemes that enable Kalman\u2010type recursions when normality or linearity or both are abandoned. The seminalbootstrap filter<\/jats:italic>(BF) of Gordon, Salmond and Smith (1993) is used to introduce the SMC jargon, potentials and limitations. We also review the literature on parameter learning, an area that started to attract much attention from the particle filter community in recent years. We give particular attention to the Liu\u2013West filter (2001), Storvik filter (2002) andparticle learning<\/jats:italic>(PL) of Carvalho, Johannes, Lopes and Polson (2010). We argue that the BF and theauxiliary particle filter<\/jats:italic>(APF) of Pitt and Shephard (1999) define two fundamentally distinct directions within the particle filter literature. We also show that the distinction is more pronounced with parameter learning and argue that PL, which follows the APF direction, is an attractive extension. One of our contributions is to sort out the research from BF to APF (during the 1990s), from APF to now (the 2000s) and from Liu\u2013West filter to Storvik filter to PL. To this end, we provide code in R for all the examples of the paper. Readers are invited to find their own way into this dynamic and active research arena.\u2003Copyright \u00a9 2010 John Wiley & Sons, Ltd.<\/jats:p>","DOI":"10.1002\/for.1195","type":"journal-article","created":{"date-parts":[[2010,7,30]],"date-time":"2010-07-30T02:01:24Z","timestamp":1280455284000},"page":"168-209","source":"Crossref","is-referenced-by-count":108,"title":["Particle filters and Bayesian inference in financial econometrics"],"prefix":"10.1002","volume":"30","author":[{"given":"Hedibert F.","family":"Lopes","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruey S.","family":"Tsay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2010,12,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1972.1100034"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00363"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2003.810284"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2003.823142"},{"key":"e_1_2_9_6_1","doi-asserted-by":"crossref","unstructured":"AndrieuC DoucetA Tadi\u0107VB.2005.On\u2010line parameter estimation in general state\u2010space models. 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Using various types of forecast error criteria, these evaluations usually conclude that the professional forecasts are little better than the no\u2010change or ARIM A type forecast. It is our contention that this conclusion is mistaken because the conventional error criteria may not capture why forecasts are ma& or how they are used. Using forecast directional accuracy, the criterion which has been found to be highly correlated with profits in an interest rate setting, we find that professional GNP<\/jats:italic> forecasts dominate the cheaper alternatives. 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However, most time\u2010series forecasting frameworks still rely solely on historical case counts and thus struggle to capture sudden shifts in population behavior. Therefore, to quantify the value of external behavioral signals during the COVID\u201019 pandemic, this research assembled a 124\u2010week (from May 31, 2020, to October 9, 2022) panel that fuses Google Community\u2010Mobility indices with standard surveillance indicators such as new cases, deaths, tests, and vaccinations plus information about population density and the Oxford policy\u2010stringency score for 20 countries spanning six continents. We proceed to assess two forecasting methodological families for predicting new cases using an 8\u2010week hold\u2010out window. The target\u2010variable\u2010only family comprised models using a 4\u2010week rolling average, autoregressive integrated moving average (ARIMA), Prophet, and long short\u2010term memory (LSTM) approaches. In contrast, the data\u2010integration family employs distinct light gradient boosting machine (LightGBM) variants: LightGBM\u2010Direct, which learns a single multi\u2010output mapping for all periods in the horizon, and LightGBM\u2010Recursive, which updates a one\u2010step model and rolls its predictions forward. Performance is evaluated using root mean square error (RMSE) and two optimized weight indices (OWIs), which benchmark improvements over the rolling\u2010average baseline and ARIMA, respectively. The results demonstrate that a mobility\u2010enhanced LightGBM achieves the lowest RMSE in every country, reducing the overall median error by 83% compared with the baseline and by 87% against ARIMA. LightGBM\u2010Direct excels in twelve nations, characterized by smoother trends, whereas LightGBM\u2010Recursive dominates in the remaining eight, which exhibit rapid fluctuations in incidence. Notably, SHapley Additive exPlanations (TreeSHAP) identifies workplace and transit\u2010station mobility, testing intensity, vaccinations, and policy stringency as the most influential predictors, denoting the importance of external behavioral signals in improving pandemic forecast accuracy.<\/jats:p>","DOI":"10.1002\/for.70006","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T08:10:42Z","timestamp":1755504642000},"page":"2405-2424","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating Google Mobility Indices for Forecasting Infectious Diseases Incidence: A Multi\u2010Country Study on COVID\u201019 With LightGBM"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0255-968X","authenticated-orcid":false,"given":"Milton","family":"Soto\u2010Ferrari","sequence":"first","affiliation":[{"name":"School of Supply Chain, Logistics & Maritime Operations Old Dominion University Norfolk Virginia USA"},{"name":"Department of Information Technology & Decision Sciences Old Dominion University Norfolk Virginia USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1108\/JPMH-07-2023-0058"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.2196\/18828"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112896"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2020.00426"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1287\/inte.2023.0009"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2020.07.007"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-36318-4_3"},{"volume-title":"Time Series Analysis: Forecasting and Control","year":"2015","author":"Box G. 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Moreover, the model provides a new fortnightly indicator of GDP, consistent with the official quarterly series.<\/jats:p>","DOI":"10.1002\/for.2565","type":"journal-article","created":{"date-parts":[[2018,12,4]],"date-time":"2018-12-04T00:04:56Z","timestamp":1543881896000},"page":"207-221","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Short\u2010term forecasts of economic activity: Are fortnightly factors useful?"],"prefix":"10.1002","volume":"38","author":[{"given":"Libero","family":"Monteforte","sequence":"first","affiliation":[{"name":"Economic Outlook and Monetary Policy Directorate Banca d'Italia Rome Italy"},{"name":"Macroeconomic analysis department Ufficio Parlamentare di Bilancio Rome Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9915-563X","authenticated-orcid":false,"given":"Valentina","family":"Raponi","sequence":"additional","affiliation":[{"name":"Imperial College Business School Imperial Colege London London UK"},{"name":"MEMOTEF, Sapienza Universit\u00e0 di Roma Rome Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,12,21]]},"reference":[{"key":"e_1_2_7_2_1","first-page":"147","article-title":"The Italian business cycle: Coincident and leading indicators and some stylized facts","volume":"60","author":"Altissimo F.","year":"2000","journal-title":"Giornale degli Economisti e Annali di Economia"},{"key":"e_1_2_7_3_1","doi-asserted-by":"crossref","unstructured":"Andreou E. 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To capture fluctuations from external information and volatility of realized volatility (RV), we incorporate the trading volume and jumping into the HAR\u2010V\u2010J model in the first place and then incorporate a GARCH specification into the HAR\u2010GARCH model. Results showed that there were large fluctuations in SHFE gold futures market before the launch of night trading sessions and mostly stemmed from overnight fluctuation in the international gold futures market. After the launch of night trading sessions, the realized volatility has a clear trend of moderation. In the in\u2010sample estimation, both jump and external information are found to have significant explanatory power with the HAR\u2010V\u2010J model. Additionally, the volatility clustering and high persistence of the realized volatility were confirmed by the GARCH coefficients. Last but not the least, night trading sessions have significantly improved the out\u2010of\u2010sample forecasting performances of realized volatility models. Among them, the HAR\u2010V\u2010J model is the best\u2010performing model. This conclusion holds for various prediction horizons and has great practical values for investors and policymakers.<\/jats:p>","DOI":"10.1002\/for.2748","type":"journal-article","created":{"date-parts":[[2020,12,12]],"date-time":"2020-12-12T07:47:12Z","timestamp":1607759232000},"page":"849-860","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Can night trading sessions improve forecasting performance of gold futures' volatility in China?"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9368-176X","authenticated-orcid":false,"given":"Xuan","family":"Yao","sequence":"first","affiliation":[{"name":"School of Management Harbin Institute of Technology China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7085-6176","authenticated-orcid":false,"given":"Xiaofeng","family":"Hui","sequence":"additional","affiliation":[{"name":"School of Management Harbin Institute of Technology China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9082-3788","authenticated-orcid":false,"given":"Kaican","family":"Kang","sequence":"additional","affiliation":[{"name":"Credit Research Initiative National University of Singapore Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-405X(01)00055-1"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0020-6598.2004.00298.x"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/13518470801892236"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbh001"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(86)90063-1"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1573-4412(05)80018-2"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.2307\/1913889"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbp001"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/07474930701853616"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-405X(02)00259-3"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.2307\/2297980"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbankfin.2014.05.026"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbi025"},{"issue":"5","key":"e_1_2_6_15_1","first-page":"207","article-title":"Research on the effect of night trading on the market of precious metal futures","volume":"29","author":"Hui X.","year":"2020","journal-title":"Operations Research Management Science"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iref.2011.06.003"},{"issue":"3","key":"e_1_2_6_17_1","first-page":"67","article-title":"Have extended trading hours made agricultural commodity markets riskier?","volume":"93","author":"Kauffman N.","year":"2013","journal-title":"Economic Review (Kansas City)"},{"key":"e_1_2_6_18_1","first-page":"1","article-title":"The impact of SHFE's night trading session on volume and realized volatility of aluminum and copper futures markets","author":"Klein T.","year":"2018","journal-title":"SSRN Electronic Journal"},{"key":"e_1_2_6_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pacfin.2014.07.005"},{"key":"e_1_2_6_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2018.07.033"},{"key":"e_1_2_6_21_1","first-page":"62","volume-title":"Modelling financial time series","author":"Taylor S. 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Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid assets, this measure is difficult to obtain for asset pricing factors such as size and value that include smaller illiquid stocks that are not traded at a high frequency. Here, we provide a simple approach to estimate the volatility of these factors. 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The reason for this failure lies in the complexity of the standard procedures. Often they require fairly complex mathematical models and use a variety of cumbersome algebraic manipulations. An alternative formulation for exponential smoothing is presented. It simplifies these procedures and allows an easier use of the full range of models. This new formulation is obtained by considering the relationship between general exponential smoothing (GES) and the well\u2010known ARMA process of Box and Jenkins. The three commonest seasonal models have only recently been considered for GES systems. They are discussed in some detail here. The computational requirements of the GES and equivalent ARMA procedures are reviewed and some recommendations for their application are made. The initialization of GES forecasting systems and the important problem of model selection is also discussed. 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We find that the LI can negatively predict stock market excess returns in\u2010sample and out\u2010of\u2010sample. In addition, the LI can generate a large economic gain for a mean\u2013variance investor. 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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":5,"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":[{"role":"author","vocabulary":"crossref"}]},{"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. Paper presented at the 2007 annual meeting of the American Political Science Association Chicago IL."},{"key":"e_1_2_7_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.2377"},{"key":"e_1_2_7_33_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0208190"}],"container-title":["Journal of Forecasting"],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Ffor.2711","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.2711","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/for.2711","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.2711","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T13:35:17Z","timestamp":1692970517000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/for.2711"}},"issued":{"date-parts":[[2020,7,6]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1002\/for.2711"],"URL":"https:\/\/doi.org\/10.1002\/for.2711","archive":["Portico"],"ISSN":["0277-6693","1099-131X"],"issn-type":[{"type":"print","value":"0277-6693"},{"type":"electronic","value":"1099-131X"}],"published":{"date-parts":[[2020,7,6]]},"assertion":[{"value":"2020-02-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-06-04","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-07-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]},{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T05:10:15Z","timestamp":1759381815361,"version":"build-2065373602"},"reference-count":0,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"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":[[2025,11]]},"abstract":"No abstract is available for this article.<\/jats:p>","DOI":"10.1002\/for.3162","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T04:30:51Z","timestamp":1759379451000},"page":"2053-2053","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Issue Information"],"prefix":"10.1002","volume":"44","member":"311","published-online":{"date-parts":[[2025,10]]},"container-title":["Journal of Forecasting"],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/for.3162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T04:31:31Z","timestamp":1759379491000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/for.3162"}},"issued":{"date-parts":[[2025,10]]},"references-count":0,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1002\/for.3162"],"URL":"https:\/\/doi.org\/10.1002\/for.3162","archive":["Portico"],"ISSN":["0277-6693","1099-131X"],"issn-type":[{"value":"0277-6693","type":"print"},{"value":"1099-131X","type":"electronic"}],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"2025-10-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]},{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T16:07:50Z","timestamp":1764432470179},"reference-count":42,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2004,11,3]],"date-time":"2004-11-03T00:00:00Z","timestamp":1099440000000},"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":[[2004,12]]},"abstract":"Abstract<\/jats:title>Financial market time series exhibit high degrees of non\u2010linear variability, and frequently have fractal properties. When the fractal dimension of a time series is non\u2010integer, this is associated with two features: (1) inhomogeneity\u2014extreme fluctuations at irregular intervals, and (2) scaling symmetries\u2014proportionality relationships between fluctuations over different separation distances. In multivariate systems such as financial markets, fractality is stochastic rather than deterministic, and generally originates as a result of multiplicative interactions. Volatility diffusion models with multiple stochastic factors can generate fractal structures. In some cases, such as exchange rates, the underlying structural equation also gives rise to fractality. Fractal principles can be used to develop forecasting algorithms. The forecasting method that yields the best results here is the state transition\u2010fitted residual scale ratio<\/jats:italic> (ST\u2010FRSR) model. A state transition model is used to predict the conditional probability of extreme events. 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In this paper, we constructed the one\u2010dimensional convolutional neural networks (1D\u2010CNN) and long short\u2010term memory (LSTM) deep learning models to investigate the feasibility of forecasting corporate financial performance with deep learning models, using the corporate financial features and environment, social and governance (ESG) rating index of Chinese A\u2010share listed corporation data from 2015 to 2021. Five evaluation metrics were employed to measure models' forecasting effects, and four competing machine learning models were built to verify the improvement in forecasting accuracy brought by the deep learning models. Furthermore, we also introduced the Accumulated Local Effects method to explore the forecasting processes of the deep learning models. 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Previous research has predominantly focused on promotional and nonpromotional periods, often overlooking the postpromotional phase, where demand decreases due to consumer stockpiling during promotions. To address this research gap, we investigate both traditional statistical forecasting methods and contemporary approaches, such as global models, implemented using gradient boosting and deep learning techniques. We assess their performance throughout the entire demand life cycle. We employ the base\u2010lift approach as our benchmark model, commonly used in the retail sector. Our study results confirm that machine learning methods effectively manage demand volatility induced by retail promotions while enhancing forecast accuracy across the demand life cycle. The base\u2010lift model performs comparably to alternative machine learning methods, albeit with the additional effort required for data cleansing. Our proposed forecasting framework possesses the capability to automate the retail forecasting process in the presence of sales promotions, facilitating efficient retail planning. 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Forecasting Methods for Management (5th edn). 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This limitation highlights the need for effective strategies that transform nonstationary time series into stationary counterparts before bootstrapping, thereby enabling the reliable application of block bootstrapping in nonstationary settings. Motivated by this issue, this study develops an enhanced bagging\u2010based approach incorporating a scaled logit transformation and a decomposition technique. In particular, the scaled logit transformation operates without parameter estimation and effectively stabilizes variance for data containing negative values or bounded ranges, such as proportions and rates, unlike a Box\u2013Cox transformation, which relies on parameter estimation and requires positive data. The effectiveness of our method is examined through two illustrative studies: a simulation study and a real data analysis. In the simulation study, its performance is evaluated using various nonstationary time series generated under controlled conditions. For the real data analysis, three nonstationary time series datasets with different frequencies are utilized to substantiate its practical applicability.<\/jats:p>","DOI":"10.1002\/for.70138","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T23:20:44Z","timestamp":1773357644000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Bagging\u2010Based Approach for Forecasting Nonstationary Time Series: Bridging Nonstationarity With a Scaled Logit Transformation"],"prefix":"10.1002","author":[{"given":"Young\u00a0Eun","family":"Jeon","sequence":"first","affiliation":[{"name":"Department of Data Science Gyeongkuk National University Andong Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongku","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Statistics Kyungpook National University Daegu Korea"},{"name":"KNU G\u2010LAMP Research Center, Institute of Basic Sciences Kyungpook National University Daegu Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jung\u2010In","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Data Science Gyeongkuk National University Andong Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2020.105340","article-title":"Application of the ARIMA Model on the COVID\u20102019 Epidemic Dataset","volume":"29","author":"Benvenuto D.","year":"2020","journal-title":"Data in Brief"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ijforecast.2015.07.002","article-title":"Bagging Exponential Smoothing Methods Using STL Decomposition and Box\u2013Cox Transformation","volume":"32","author":"Bergmeir C.","year":"2016","journal-title":"International Journal of Forecasting"},{"key":"e_1_2_9_4_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1111\/j.2517-6161.1964.tb00553.x","article-title":"An Analysis of Transformations","volume":"26","author":"Box G. E. P.","year":"1964","journal-title":"Journal of the Royal Statistical Society Series B: Statistical Methodology"},{"key":"e_1_2_9_5_1","first-page":"3","article-title":"STL: A Seasonal\u2010Trend Decomposition Procedure Based on Loess","volume":"6","author":"Cleveland R. B.","year":"1990","journal-title":"Journal of Official Statistics"},{"key":"e_1_2_9_6_1","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1016\/j.ijforecast.2018.05.006","article-title":"Improving Time Series Forecasting: An Approach Combining Bootstrap Aggregation, Clusters and Exponential Smoothing","volume":"34","author":"Dantas T. M.","year":"2018","journal-title":"International Journal of Forecasting"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.jairtraman.2016.12.006","article-title":"Air Transportation Demand Forecast Through Bagging Holt Winters Methods","volume":"59","author":"Dantas T. M.","year":"2017","journal-title":"Journal of Air Transport Management"},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1016\/j.procs.2022.01.298","article-title":"Predictive Analytics for Demand Forecasting\u2014A Comparison of SARIMA and LSTM in Retail SCM","volume":"200","author":"Falatouri T.","year":"2022","journal-title":"Procedia Computer Science"},{"key":"e_1_2_9_9_1","doi-asserted-by":"crossref","unstructured":"Hyndman R.2018. \u201cfpp2: Data for \u201cForecasting: Principles and Practice\u201d.\u201d R Package Version 2.3.","DOI":"10.32614\/CRAN.package.fpp2"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.renene.2020.02.117","article-title":"Hydroelectricity Consumption Forecast for Pakistan Using ARIMA Modeling and Supply\u2010Demand Analysis for the Year 2030","volume":"154","author":"Jamil R.","year":"2020","journal-title":"Renewable Energy"},{"key":"e_1_2_9_11_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117366","article-title":"Bagging Ensemble\u2010Based Novel Data Generation Method for Univariate Time Series Forecasting","volume":"203","author":"Kim D.","year":"2022","journal-title":"Expert Systems With Applications"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1214\/aos\/1176347265","article-title":"The Jackknife and the Bootstrap for General Stationary Observations","volume":"17","author":"K\u00fcnsch H. R.","year":"1989","journal-title":"Annals of Statistics"},{"key":"e_1_2_9_13_1","doi-asserted-by":"crossref","DOI":"10.3390\/en14196021","article-title":"Forecasting Natural Gas Production and Consumption in United States\u2014Evidence From SARIMA and SARIMAX Models","volume":"14","author":"Manigandan P.","year":"2021","journal-title":"Energies"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eneco.2021.105760","article-title":"Forecasting Natural Gas Consumption Using Bagging and Modified Regularization Techniques","volume":"106","author":"Meira E.","year":"2022","journal-title":"Energy Economics"},{"key":"e_1_2_9_15_1","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1016\/j.dsx.2020.07.042","article-title":"ARIMA Modelling & Forecasting of COVID\u201019 in Top Five Affected Countries","volume":"14","author":"Sahai A. 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In this paper, based on real operation data from 2015 to 2016 at several stations along the Wuhan\u2013Guangzhou high\u2010speed railway, NAT and TTAT influencing factors were determined after analyzing the PD propagation mechanism. The eXtreme Gradient BOOSTing (XGBOOST) algorithm was used to establish a NAT predictive model, and several machine learning methods were compared. The importance of different delayinfluencing factors was investigated. Then, the TTAT predictive model (using support vector regression (SVR) algorithms) was established based on the NAT predictive model. Results indicated that the XGBOOST algorithm performed well with the NAT predictive model, and SVR was the optimal model for TTAT prediction under the verification index (i.e., the ratio of the difference between the actual and predicted value was less than 1\/2\/3\/4\/5 min). Real operational data in 2018 were used to test the applicability of the NAT and TTAT models over time, and findings suggest that these models exhibit sound applicability over time based on XGBOOST and SVR, respectively.<\/jats:p>","DOI":"10.1002\/for.2685","type":"journal-article","created":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T05:03:41Z","timestamp":1585112621000},"page":"1198-1212","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Predictive models for influence of primary delays using high\u2010speed train operation records"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7123-5198","authenticated-orcid":false,"given":"Zhongcan","family":"Li","sequence":"first","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3933-2446","authenticated-orcid":false,"given":"Chao","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixiong","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2020,4,13]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.05.019"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"Chen T. &Guestrin C.(2016).Xgboost: A scalable tree boosting system. 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IEEE.: New York NY.","DOI":"10.1109\/ITSC.2010.5625081"},{"key":"e_1_2_8_12_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/8639589","article-title":"Modeling the influence of disturbances in high\u2010speed railway systems","volume":"2019","author":"Huang P.","year":"2019","journal-title":"Journal of Advanced Transportation"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0191-2615(99)00051-X"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(01)00269-7"},{"key":"e_1_2_8_15_1","unstructured":"INFORMS. (2018).RAS problem solving competition: Train delay forecasting. Retrieved fromhttp:\/\/connect.informs.org\/railway-applications\/awards\/problem-solving-competition\/new-item2."},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1139\/cjce-2017-0642"},{"key":"e_1_2_8_17_1","unstructured":"Kecman P. Corman F. &Meng L.(2015).Train delay evolution as a stochastic process. 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Such risks may propagate through shareholding relationships, potentially amplifying financial distress and threatening the stability of the broader system. Enterprise risks originate not only from internal factors but also from complex equity relationship networks. Consequently, there is a critical need for enterprise bankruptcy prediction models to support investment and operational risk management. We propose hypergraphs and bidirectional attention\u2010based dual graph neural networks (HBA\u2010DGNN) as an innovative approach for predicting enterprise bankruptcy. It consists of two main components. The first component, the hypergraph embeddings of categorical features (HECF) module, can effectively capture higher order relationships among enterprises. Simultaneously, the bidirectional attention\u2010based GNN (BAG) module quantifies the importance of equity relationships based on enterprise attributes and networks. We conduct an empirical study on the model of Evergrande Group, which faced a debt crisis and caused systemic risks in China's financial market. The HBA\u2010DGNN demonstrates superior predictive performance compared to baseline models, achieving an average improvement of over 20%. Additionally, attention coefficients in the BAG significantly correlate with enterprise bankruptcy, effectively identifying critical edges and nodes responsible for risk contagion. 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Accurate wind forecasting can mitigate these challenges and help in integrating more wind power into the grid. A range of studies have presented algorithms to forecast the wind in terms of wind speeds and wind power generation across different timescales. However, the classification of timescales varies significantly across the different studies (2010\u20132014). 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Specifically of importance is whether the Henderson moving\u2010average procedure distorts the underlying time\u2010series properties of the data for forecasting purposes. Given the weight of attention in the literature to the seasonal adjustment process used by various statistical agencies, this study hopes to address the dearth of literature on \u2018trending\u2019 procedures. Forecasts using both the trended and untrended series are generated. The forecasts are then made comparable by \u2018detrending\u2019 the trended forecasts, and comparing both series to the realised values. Forecasting accuracy is measured by a suite of common methods, and a test of significance of difference is applied to the respective root mean square errors. 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The evidence in Australia indicates that computer based forecasting systems are not being widely used and in fact a number of established systems have been discarded, with the issue of forecast accuracy often being mentioned as a problem area. Two experiments are carried out to examine this issue by comparing judgemental and quantitative forecasts. Other problem areas mentioned as contributing to the abandonment of forecasting systems include the difficulty of manually reviewing the computer forecasts and the effort required to carefully massage the forecast database to remove extraordinary events.<\/jats:p>","DOI":"10.1002\/for.3980020207","type":"journal-article","created":{"date-parts":[[2007,7,7]],"date-time":"2007-07-07T22:48:21Z","timestamp":1183848501000},"page":"169-179","source":"Crossref","is-referenced-by-count":57,"title":["An exploration of some practical issues in the use of quantitative forecasting models"],"prefix":"10.1002","volume":"2","author":[{"given":"M. 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However, when used in the prediction of daily tourism volume, the feature variables of the huge and complex search engine data do not have strong independence. These repetitive and highly relevant data must be analyzed and selected; otherwise, they will increase the training burden of neural network and reduce the prediction effect. This study proposes a daily tourism volume prediction model, maximum correlation minimum redundancy feature selection and long short\u2010term memory, on the basis of feature selection and deep learning. Firstly, the multivariate high\u2010dimensional features, including search engine data and weather factors, are selected to identify the key influencing factors. Secondly, the deep neural network is used to make a multistep forward rolling prediction of daily tourism volume. 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In the USA and some other countries, composite indexes of coincident indicators (CEI) are used to date classical business cycle turning points; also indexes of leading indicators (LEI) are used to help in the difficult task of predicting these turning points. This paper reviews a selection of the available data for monthly and quarterly euro area coincident and leading indicators. From these data, we develop composite indexes using methods analogous to those tested in the US CEI and LEI published by The Conference Board. 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The area of agriculture is attracting growing attention because of increasing the demand for food supplies. To ensure future food supplies, crop yield prediction (CYP) provides the best decision\u2010making to assist farmers in agricultural yield forecasting efficiently. Nevertheless, CYP is a difficult endeavor because of the intricacy of the underlying mechanisms and the effect of numerous factors, including weather patterns, soil characteristics, and crop management techniques. In today's era, ensemble learning (EL) approaches have recently demonstrated significant promise for enhancing the reliability and accuracy of CYP. The success of the EL techniques depends on several facts, including how the base learner models are trained and how these are combined. This study provides important insights into the EL techniques for CYP. This paper proposes an expert system model named precise ensemble expert system for crop yield prediction (PEESCYP) to predict the best crop for agricultural land. The proposed PEESCYP model employs multiple imputation by chained equation (MICE) data imputation technique to treat the missing values of the collected dataset, the isolation forest (IF) technique for outlier detection, the ant colony optimization (ACO) technique to perform feature selection, robust scaling (RS) technique to perform data normalization, and the extra tree (ET) is used for classification to overcome the variance and overfitting problem of the single classifiers. The measurements of the proposed PEESCYP model have been collected by means of accuracy, precision, recall, and F\u20101 score using a prepared dataset, which is collected from International Crops Research Institute for the Semi\u2010Arid Tropics (ICRISAT), and the proposed model is compared with different single\u2010classifier based ML models, EL models, and various existing models available in the literature. The results of this experiment underline that the proposed PEESCYP model outperforms the others.<\/jats:p>","DOI":"10.1002\/for.3183","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T01:59:30Z","timestamp":1722909570000},"page":"3161-3176","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Design of a precise ensemble expert system for crop yield prediction using machine learning analytics"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1406-0808","authenticated-orcid":false,"given":"Deeksha","family":"Tripathi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering National Institute of Technology Silchar India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saroj K.","family":"Biswas","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering National Institute of Technology Silchar India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,8,5]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e13339"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119421566.ch2"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gltp.2021.08.060"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09569-8"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/bs.agron.2018.11.002"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058655"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.05.012"},{"key":"e_1_2_10_9_1","unstructured":"Dev S.M. andSharma A.N. 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We draw our empirical results by fitting vine copulas (e.g., r\u2010vines, c\u2010vines, d\u2010vines), IGARCH(1,1) RiskMetrics value\u2010at\u2010risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value\u2010at\u2010risk, conditional drawdown\u2010at\u2010risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. 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Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine\u2010to\u2010coarse reconstruction algorithm, the high\u2010frequency, low\u2010frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high\u2010frequency components. LSSVM is suitable for forecasting the low\u2010frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. 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Previous studies have encountered difficulties in forecasting highly volatile crude oil prices, especially when conflicts, wars, and other irregular events occur. In light of this, this study introduces an innovative hybrid multifactor decomposition\u2013ensemble approach with heterogeneous data from diverse sources. First, the multivariate forecasters including unstructured news text based on keywords and structured financial variables are processed. Second, multivariate empirical mode decomposition (MEMD) is used to decompose the crude oil price and its predictors, and sample entropy (SE) is employed to reconstruct the subcomponents obtained from the decomposition. Thereafter, some effective forecasters are screened from the reconstructed subcomponents of forecasters through statistically testing approaches. Finally, the crude oil prices are forecasted using a hybrid forecasting technique, and the performance of the proposed model is assessed from various viewpoints. The empirical conclusions indicate that the proposed model performs well in forecasting the weekly spot price of West Texas Intermediate crude oil.<\/jats:p>","DOI":"10.1002\/for.70160","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T02:11:35Z","timestamp":1776737495000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A New Multivariate Decomposition\u2013Ensemble Approach With Multisource Heterogeneous Data for Crude Oil Price Forecasting"],"prefix":"10.1002","author":[{"given":"Zhengling","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Economics Northwest Normal University Lanzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyun","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Statistics and Data Science Lanzhou University of Finance and Economics Lanzhou 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attention over the past forty years. In the 1950s and 1960s most of these forecasts and analyses were generated by simultaneous equation econometric models. Beginning in the 1970s, there was a shift in the modeling of economic variables from the structural equations approach with strong identifying restrictions towards a joint time\u2010series model with very few restrictions. One such model is the vector auto regression (VAR) model. It was soon discovered that the unrestricted VAR models do not forecast well. The Bayesian vector auto regression (BVAR) approach as well the error correction model (ECM) and models based on the theory of co integration have been offered as alternatives to the simple VAR model. This paper argues that the BVAF., ECM, and co integration models are simply VAR models with various restrictions placed on the coefficients. Based on this notion of a restricted VAR model, a four\u2010step procedure for specifying VAR forecasting models is presented and then applied to monthly data on US electricity consumption and prices.<\/jats:p>","DOI":"10.1002\/for.3980140310","type":"journal-article","created":{"date-parts":[[2007,7,8]],"date-time":"2007-07-08T23:46:40Z","timestamp":1183938400000},"page":"287-310","source":"Crossref","is-referenced-by-count":25,"title":["An integrated bayesian vector auto regression and error correction model for forecasting electricity consumption and prices"],"prefix":"10.1002","volume":"14","author":[{"given":"Frederick L","family":"Joutz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. 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Forecasts are typically produced either from economic theory\u2010based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can hardly be beaten if they are carefully specified. 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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 (>\u2009200%) than the returns generated based on technical indicators, as suggested by existing studies. 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