{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T03:34:25Z","timestamp":1785468865100,"version":"3.56.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s00500-021-05830-1","type":"journal-article","created":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T09:02:51Z","timestamp":1620896571000},"page":"7887-7898","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Forecasting foreign exchange markets: further evidence using machine learning models"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3794-6161","authenticated-orcid":false,"given":"Paravee","family":"Maneejuk","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wilawan","family":"Srichaikul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"issue":"3","key":"5830_CR1","first-page":"01","volume":"4","author":"AS Babu","year":"2015","unstructured":"Babu AS, Reddy SK (2015) Exchange rate forecasting using ARIMA. Neural Netw Fuzzy Neuron J Stock Forex Trading 4(3):01\u201305","journal-title":"Neural Netw Fuzzy Neuron J Stock Forex Trading"},{"issue":"7","key":"5830_CR2","doi-asserted-by":"publisher","first-page":"e0180944","DOI":"10.1371\/journal.pone.0180944","volume":"12","author":"W Bao","year":"2017","unstructured":"Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 12(7):e0180944","journal-title":"PLoS ONE"},{"key":"5830_CR3","first-page":"273","volume":"20","author":"J Behar","year":"1995","unstructured":"Behar J (1995) Support vector machines. Learning 20:273\u2013297","journal-title":"Learning"},{"key":"5830_CR4","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural networks for pattern recognition","author":"CM Bishop","year":"1995","unstructured":"Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford"},{"key":"5830_CR5","volume-title":"Time series analysis: forecasting and control","author":"GE Box","year":"2015","unstructured":"Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, New York"},{"issue":"2","key":"5830_CR6","first-page":"179","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman JL (1990) Finding structure in time. CognitSci 14(2):179\u2013211","journal-title":"CognitSci"},{"key":"5830_CR7","doi-asserted-by":"crossref","unstructured":"Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, vol 1, no 10. Springer series in statistics, New York","DOI":"10.1007\/978-0-387-21606-5_1"},{"issue":"4","key":"5830_CR8","first-page":"100","volume":"24","author":"S Galeshchuk","year":"2017","unstructured":"Galeshchuk S, Mukherjee S (2017b) Deep networks for predicting direction of change in foreign exchange rates. IntellSyst Account Finance Manag 24(4):100\u2013110","journal-title":"IntellSyst Account Finance Manag"},{"key":"5830_CR9","doi-asserted-by":"crossref","unstructured":"Galeshchuk S, Mukherjee S (2017a) Deep learning for predictions in emerging currency markets. In: International conference on agents and artificial intelligence, vol 2. SCITEPRESS, pp 681\u2013686","DOI":"10.5220\/0006250506810686"},{"issue":"4","key":"5830_CR10","first-page":"39","volume":"7","author":"A Hadjixenophontos","year":"2017","unstructured":"Hadjixenophontos A, Christodoulou-Volos C (2017) Predictability of foreign exchange rates with the AR (1) model. J Appl Finance Bank 7(4):39\u201358","journal-title":"J Appl Finance Bank"},{"key":"5830_CR11","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1146\/annurev-statistics-031017-100307","volume":"5","author":"L Held","year":"2018","unstructured":"Held L, Ott M (2018) On p-values and Bayes factors. Annu Rev Stat Appl 5:393\u2013419","journal-title":"Annu Rev Stat Appl"},{"issue":"1","key":"5830_CR13","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.eswa.2009.05.044","volume":"37","author":"M Khashei","year":"2010","unstructured":"Khashei M, Bijari M (2010) An artificial neural network (p, d, q) model for time series forecasting. Expert SystAppl 37(1):479\u2013489","journal-title":"Expert SystAppl"},{"key":"5830_CR14","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.neucom.2004.04.002","volume":"61","author":"TY Kim","year":"2004","unstructured":"Kim TY, Oh KJ, Kim C, Do JD (2004) Artificial neural networks for non-stationary time series. Neurocomputing 61:439\u2013447","journal-title":"Neurocomputing"},{"issue":"2","key":"5830_CR15","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1002\/for.2628","volume":"39","author":"F Kunze","year":"2020","unstructured":"Kunze F (2020) Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts. J Forecast 39(2):313\u2013333","journal-title":"J Forecast"},{"key":"5830_CR16","doi-asserted-by":"publisher","DOI":"10.1002\/047084535X","volume-title":"Recurrent neural networks for prediction: learning algorithms, architectures, and stability","author":"D Mandic","year":"2001","unstructured":"Mandic D, Chambers J (2001) Recurrent neural networks for prediction: learning algorithms, architectures, and stability. Wiley, New York"},{"key":"5830_CR17","first-page":"1","volume":"48","author":"P Maneejuk","year":"2020","unstructured":"Maneejuk P, Yamaka W (2020) Significance test for linear regression: how to test without P-values? J Appl Stat 48:1\u201319","journal-title":"J Appl Stat"},{"issue":"4","key":"5830_CR18","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1257\/jel.45.4.936","volume":"45","author":"L Menkhoff","year":"2007","unstructured":"Menkhoff L, Taylor MP (2007) The obstinate passion of foreign exchange professionals: technical analysis. J Econ Lit 45(4):936\u2013972","journal-title":"J Econ Lit"},{"key":"5830_CR19","first-page":"2015","volume":"2014","author":"TMU Ngan","year":"2013","unstructured":"Ngan TMU (2013) Forecasting foreign exchange rate by using ARIMA model: a case of VND\/USD exchange rate. Methodology 2014:2015","journal-title":"Methodology"},{"issue":"1","key":"5830_CR20","first-page":"3","volume":"26","author":"A Parot","year":"2019","unstructured":"Parot A, Michell K, Kristjanpoller WD (2019) Using Artificial Neural Networks to forecast Exchange Rate, including VAR-VECM residual analysis and prediction linear combination. IntellSyst Account Finance Manag 26(1):3\u201315","journal-title":"IntellSyst Account Finance Manag"},{"issue":"7","key":"5830_CR21","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1002\/for.2354","volume":"34","author":"V Plakandaras","year":"2015","unstructured":"Plakandaras V, Papadimitriou T, Gogas P (2015) Forecasting daily and monthly exchange rates with machine learning techniques. J Forecast 34(7):560\u2013573","journal-title":"J Forecast"},{"issue":"1","key":"5830_CR22","doi-asserted-by":"publisher","first-page":"e0227222","DOI":"10.1371\/journal.pone.0227222","volume":"15","author":"J Qiu","year":"2020","unstructured":"Qiu J, Wang B, Zhou C (2020) Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS ONE 15(1):e0227222","journal-title":"PLoS ONE"},{"issue":"1","key":"5830_CR23","first-page":"7","volume":"26","author":"M Rout","year":"2014","unstructured":"Rout M, Majhi B, Majhi R, Panda G (2014) Forecasting of currency exchange rates using an adaptive ARMA model with differential evolution-based training. J King Saud Univ Computer InfSci 26(1):7\u201318","journal-title":"J King Saud Univ Computer InfSci"},{"key":"5830_CR24","first-page":"108","volume":"524","author":"R Ruby-Figueroa","year":"2017","unstructured":"Ruby-Figueroa R, Saavedra J, Bahamonde N, Cassano A (2017) Permeate flux prediction in the ultrafiltration of fruit juices by ARIMA models. J MembrSci 524:108\u2013116","journal-title":"J MembrSci"},{"key":"5830_CR25","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"2013","unstructured":"Vapnik V (2013) The nature of statistical learning theory. Springer, Berlin"},{"issue":"02","key":"5830_CR26","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1142\/S0219024999000145","volume":"2","author":"J Yao","year":"1999","unstructured":"Yao J, Tan CL, Poh HL (1999) Neural networks for technical analysis: a study on KLCI. Int J TheorAppl Finance 2(02):221\u2013241","journal-title":"Int J TheorAppl Finance"},{"issue":"6","key":"5830_CR27","first-page":"468","volume":"6","author":"J Yue","year":"2015","unstructured":"Yue J, Zhao W, Mao S, Liu H (2015) Spectral\u2013spatial classification of hyperspectral images using deep convolutional neural networks. Remote SensLett 6(6):468\u2013477","journal-title":"Remote SensLett"},{"issue":"1","key":"5830_CR28","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","volume":"14","author":"G Zhang","year":"1998","unstructured":"Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14(1):35\u201362","journal-title":"Int J Forecast"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-05830-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-021-05830-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-05830-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T17:51:03Z","timestamp":1699033863000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-021-05830-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,13]]},"references-count":27,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["5830"],"URL":"https:\/\/doi.org\/10.1007\/s00500-021-05830-1","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,13]]},"assertion":[{"value":"16 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}