{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:31:52Z","timestamp":1760711512555,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>The prediction of Shanghai composite index is one of the hot topics in financial research. In this paper, based on empirical mode decomposition (EMD) algorithm combined with nonlinear autoregressive models with exogenous inputs (NARX) neural network theory, an EMD-NARX combination forecasting model is proposed. Firstly, the EMD algorithm is used to decompose the time series data into the intrinsic mode function components of different scales, and then the NARX neural network is used to predict the future index with multi step rolling forecast. The experiments show that the presented method not only can make accurate short-term prediction of Shanghai stock index, but also can predict the Shanghai index directly on the long-run trend. The method breaks through the limitations of traditional forecasting methods in long-term prediction.<\/jats:p>","DOI":"10.3233\/978-1-61499-785-6-590","type":"book-chapter","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:06:08Z","timestamp":1740053168000},"source":"Crossref","is-referenced-by-count":1,"title":["Study on Prediction of the Shanghai Composite Index Based on EMD and NARX Neural Network"],"prefix":"10.3233","author":[{"family":"Xiu Yan","sequence":"additional","affiliation":[]},{"family":"Chen Xinye","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Information Technology and Intelligent Transportation Systems"],"original-title":[],"deposited":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:22:26Z","timestamp":1740054146000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-784-9&spage=590&doi=10.3233\/978-1-61499-785-6-590"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-785-6-590","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2017]]}}}