{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:39:05Z","timestamp":1743046745696,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819913534"},{"type":"electronic","value":"9789819913541"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-1354-1_7","type":"book-chapter","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T14:03:27Z","timestamp":1680098607000},"page":"63-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SP-LAN: A Stock Prediction Model Based on LSTM-Attention Network"],"prefix":"10.1007","author":[{"given":"Jingyou","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Kou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peixuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,30]]},"reference":[{"key":"7_CR1","unstructured":"Li, T.: Research on stock fluctuation prediction method based on wavelet analysis and BP neural network. Tianjin University (2018)"},{"key":"7_CR2","first-page":"158","volume":"10","author":"XQ Li","year":"2017","unstructured":"Li, X.Q.: Research and application of grey prediction in stock price. Times Finance 10, 158\u2013160 (2017)","journal-title":"Times Finance"},{"issue":"2","key":"7_CR3","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.ejor.2018.05.026","volume":"271","author":"CA Tsiliyannis","year":"2018","unstructured":"Tsiliyannis, C.A.: Markov chain modeling and prediction of product returns in remanufacturing based on stock mean-age. Eur. J. Oper. Res. 271(2), 474\u2013489 (2018)","journal-title":"Eur. J. Oper. Res."},{"key":"7_CR4","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.asoc.2018.02.055","volume":"67","author":"RW Kristjanpolleri","year":"2018","unstructured":"Kristjanpolleri, R.W., Michell, V.K.: A stock market risk forecasting model through integration of switching regime, ANFIS and GARCH techniques. Appl. Soft Comput. 67, 106\u2013116 (2018)","journal-title":"Appl. Soft Comput."},{"key":"7_CR5","unstructured":"Zhang, W.X.: BP neural network stock prediction model based on PSO optimization. Harbin Institute of Technology (2010)"},{"issue":"5","key":"7_CR6","doi-asserted-by":"publisher","first-page":"8849","DOI":"10.1016\/j.eswa.2008.11.028","volume":"36","author":"YD Zhang","year":"2009","unstructured":"Zhang, Y.D., Wu, L.N.: Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Syst. Appl. 36(5), 8849\u20138854 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"10","key":"7_CR7","doi-asserted-by":"publisher","first-page":"2833","DOI":"10.1162\/neco_a_01124","volume":"30","author":"TW Gao","year":"2018","unstructured":"Gao, T.W., Chai, Y.T.: Improving stock closing price prediction using recurrent neural network and technical indicators. Neural Comput. 30(10), 2833\u20132854 (2018)","journal-title":"Neural Comput."},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Naik, N., Mohan, B.R.: Study of stock return predictions using recurrent neural networks with LSTM. In: Proceedings of International Conference on Engineering Applications of Neural Networks, pp. 453\u2013459 (2019)","DOI":"10.1007\/978-3-030-20257-6_39"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.eswa.2018.03.002","volume":"103","author":"HY Kim","year":"2018","unstructured":"Kim, H.Y., Won, C.H.: Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst. Appl. 103, 25\u201337 (2018)","journal-title":"Expert Syst. Appl."},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Zhan, X.K., Li, Y.H., Li, R.X., et al.: Stock price prediction using time convolution long short-term memory network. In: Proceedings of International Conference on Knowledge Science, Engineering and Management, pp. 461\u2013468 (2018)","DOI":"10.1007\/978-3-319-99365-2_41"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Huang, B., Ding, Q., Sun, G.Z., et al.: Stock prediction based on Bayesian-LSTM. In: Proceedings of the 2018 10th International Conference on Machine Learning and Computing, pp. 128\u2013133 (2018)","DOI":"10.1145\/3195106.3195170"},{"key":"7_CR12","unstructured":"Sun, R.Q.: Research on price trend prediction model of U.S. stock index based on LSTM neural network. Capital University of Economics and Business (2016)"},{"issue":"1","key":"7_CR13","doi-asserted-by":"publisher","first-page":"211","DOI":"10.2298\/CSIS170125042T","volume":"15","author":"Z Tao","year":"2018","unstructured":"Tao, Z., Hou, M.Z., Liu, C.H.: Prediction stock index with multi-objective optimization model based on optimized neural network architecture avoiding overfitting. Comput. Sci. Inf. Syst. 15(1), 211\u2013236 (2018)","journal-title":"Comput. Sci. Inf. Syst."},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Tan, J.H., Wang, J., Rinprasertmeechai, D., et al.: A tensor-based eLSTM model to predict stock price using financial news. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, pp. 1\u201310 (2019)","DOI":"10.24251\/HICSS.2019.201"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Matsubara, T., Akita, R., Uehara, K.: Stock price prediction by deep neural generative model of news articles. IEICE Trans. Inf. Syst. 101-D(4), 901\u2013908 (2018)","DOI":"10.1587\/transinf.2016IIP0016"},{"issue":"1","key":"7_CR16","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1007\/s10586-017-0803-x","volume":"20","author":"GW Zhang","year":"2017","unstructured":"Zhang, G.W., Xu, L.Y., Xue, Y.L.: Model and forecast stock market behavior integrating investor sentiment analysis and transaction data. Clust. Comput. 20(1), 789\u2013803 (2017). https:\/\/doi.org\/10.1007\/s10586-017-0803-x","journal-title":"Clust. Comput."},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Liu, J., Lu, Z.C., Du, W.: Combining enterprise knowledge graph and news sentiment analysis for stock price prediction. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, pp. 1\u20139 (2019)","DOI":"10.24251\/HICSS.2019.153"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Vanstone, B.J., Gepp, A., Harris, G.: The effect of sentiment on stock price prediction. In: Proceedings of International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 551\u2013559 (2018)","DOI":"10.1007\/978-3-319-92058-0_53"},{"key":"7_CR19","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.dss.2018.06.008","volume":"112","author":"S Feuerriegel","year":"2018","unstructured":"Feuerriegel, S., Gordon, J.: Long-term stock index forecasting based on text mining of regulatory disclosures. Decis. Support Syst. 112, 88\u201397 (2018)","journal-title":"Decis. Support Syst."},{"issue":"4","key":"7_CR20","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.1007\/s10664-018-9679-5","volume":"24","author":"M Kondo","year":"2019","unstructured":"Kondo, M., Bezemer, C.-P., Kamei, Y., Hassan, A.E., Mizuno, O.: The impact of feature reduction techniques on defect prediction models. Empir. Softw. Eng. 24(4), 1925\u20131963 (2019). https:\/\/doi.org\/10.1007\/s10664-018-9679-5","journal-title":"Empir. Softw. Eng."},{"key":"7_CR21","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762v5 (2017)"}],"container-title":["Communications in Computer and Information Science","Web and Big Data. APWeb-WAIM 2022 International Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-1354-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T14:04:17Z","timestamp":1680098657000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-1354-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819913534","9789819913541"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-1354-1_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb-waim2022.com\/proceedings","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"297","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5 Demo papers + 23 workshop papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}