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Finally, it uses the ensemble model to predict stock index time series data. This paper uses the Shanghai Composite index, CSI 300 index and Shenzhen Composite index as experimental data sets, and uses the BP model, CNN model and LSTM model as comparative models to conduct an experimental analysis. The experimental results show that the new ensemble learning model proposed in this paper has certain advantages in the research of stock index time series prediction.<\/jats:p>","DOI":"10.3233\/jcm-226523","type":"journal-article","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T11:28:57Z","timestamp":1668166137000},"page":"63-74","source":"Crossref","is-referenced-by-count":2,"title":["Stock index time series prediction based on ensemble learning model"],"prefix":"10.1177","volume":"23","author":[{"given":"Mei","family":"Sun","sequence":"first","affiliation":[{"name":"School of Public Finance and Taxation, Shandong University of Finance and Economics, Jinan, Shandong, China"}]},{"given":"Jihou","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong, China"}]},{"given":"Qingtao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong, China"}]},{"given":"Jiaqian","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong, China"}]},{"given":"Chaoran","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong, China"}]},{"given":"Muwei","family":"Jian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong, China"}]}],"member":"179","reference":[{"issue":"8","key":"10.3233\/JCM-226523_ref1","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2012","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"issue":"6","key":"10.3233\/JCM-226523_ref2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Communications of the ACM."},{"key":"10.3233\/JCM-226523_ref3","doi-asserted-by":"crossref","unstructured":"Hinton G, Deng L, Yu D, Dahl GE, Mohamed A, Jaitly N, et al. 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