Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154501
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dc.contributor.authorWang, Yaolien_US
dc.contributor.authorWang, Lipoen_US
dc.contributor.authorYang, Fangjunen_US
dc.contributor.authorDi, Wenxiaen_US
dc.contributor.authorChang, Qingen_US
dc.date.accessioned2021-12-23T08:05:41Z-
dc.date.available2021-12-23T08:05:41Z-
dc.date.issued2021-
dc.identifier.citationWang, Y., Wang, L., Yang, F., Di, W. & Chang, Q. (2021). Advantages of direct input-to-output connections in neural networks : the Elman network for stock index forecasting. Information Sciences, 547, 1066-1079. https://dx.doi.org/10.1016/j.ins.2020.09.031en_US
dc.identifier.issn0020-0255en_US
dc.identifier.urihttps://hdl.handle.net/10356/154501-
dc.description.abstractThe Elman neural network (ElmanNN) is well-known for its capability of processing dynamic information, which has led to successful applications in stock forecasting. In this paper, we introduce direct input-to-output connections (DIOCs) into the ElmanNN and show that the proposed Elman neural network with DIOCs (Elman-DIOCs) significantly out-performs the original ElmanNN without such DIOCs. Four different global stock indices, i.e., the Shanghai Stock Exchange (SSE) Composite Index, the Korea Stock Price Index (KOSPI), the Nikkei 225 Index (Nikkei225), and the Standard & Poor's 500 Index (SPX), are used to demonstrate the affecacy of the Elman-DIOCs in time-series prediction. We systematically evaluate 8 models, depending whether or not there are hidden layer biases, whether or not there are output layer biases, and whether or not there are DIOCs. The experimental results show that DIOCs lead to much better prediction accuracy, while requiring fewer than a half of the hidden neurons. Take the SPX index, for example - the root mean squared error (RMSE) and the mean absolute error (MAE) of the Elman-DIOCs are improved by 44.2% and 41.1%, respectively, compared to the ElmanNN, and 65.6% and 60.8%, respectively, compared to the multi-layer perceptron (MLP). We argue that (1) DIOCs can always help to improve accuracy, while reducing network complexity and computational burden, as long as the problem at hand (either regression or classification) has linear components, and (2) most real-world applications contain linear components. Therefore DIOCs will be almost always beneficial in any types of neural networks for classification or regression. We also point out that in rare cases where the problem at hand is entirely nonlinear, DIOCs should not be used.en_US
dc.language.isoenen_US
dc.relation.ispartofInformation Sciencesen_US
dc.rights© 2020 Elsevier Inc. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleAdvantages of direct input-to-output connections in neural networks : the Elman network for stock index forecastingen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1016/j.ins.2020.09.031-
dc.identifier.scopus2-s2.0-85092258605-
dc.identifier.volume547en_US
dc.identifier.spage1066en_US
dc.identifier.epage1079en_US
dc.subject.keywordsDirect Input-To-Output Connectionsen_US
dc.subject.keywordsThe Elman Neural Networken_US
dc.description.acknowledgementThis study is funded by Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao (Grant No. 61828601), Natural Science Foundation of Shanxi Province (Grant No. 201801D121141), and Provincial Program on Key Research Projects of Shanxi (Social Development Area) (Grant No. 201903D321003).en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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