{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:48:14Z","timestamp":1743065294634,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030336165"},{"type":"electronic","value":"9783030336172"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-33617-2_21","type":"book-chapter","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:04:05Z","timestamp":1573085045000},"page":"191-199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Machine Learning-Based Stock Prediction Using Financial Time Series Technical Indicators"],"prefix":"10.1007","author":[{"given":"Ahmed K.","family":"Taha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed H.","family":"Kholief","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Walid","family":"AbdelMoez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,18]]},"reference":[{"key":"21_CR1","unstructured":"Taha, A.: feature importance for ml stock prediction. https:\/\/github.com\/ahmedengu\/feature_importance"},{"key":"21_CR2","doi-asserted-by":"publisher","first-page":"75","DOI":"10.2469\/faj.v51.n1.1861","volume":"51","author":"EF Fama","year":"1995","unstructured":"Fama, E.F.: Random walks in stock market prices. Financ. Anal. J. 51, 75\u201380 (1995). https:\/\/doi.org\/10.2469\/faj.v51.n1.1861","journal-title":"Financ. Anal. J."},{"key":"21_CR3","doi-asserted-by":"publisher","first-page":"383","DOI":"10.2307\/2325486","volume":"25","author":"EF Fama","year":"1970","unstructured":"Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Finance. 25, 383 (1970). https:\/\/doi.org\/10.2307\/2325486","journal-title":"J. Finance."},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"L\u00f3pez de Prado, M.M.: Advances in financial machine learning (2018)","DOI":"10.2139\/ssrn.3365271"},{"key":"21_CR5","volume-title":"Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications","author":"JJ Murphy","year":"1999","unstructured":"Murphy, J.J., Murphy, J.J.: Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. Institute of Finance, New York (1999)"},{"key":"21_CR6","unstructured":"Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep Learning for Event-Driven Stock Prediction. aaai.org"},{"key":"21_CR7","unstructured":"Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., Deng, X.: Exploiting topic based Twitter sentiment for stock prediction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2 Short Pap.), pp. 24\u201329 (2013)"},{"key":"21_CR8","doi-asserted-by":"publisher","unstructured":"Jung, H.J., Aggarwal, J.K.: A binary stock event model for stock trends forecasting: Forecasting stock trends via a simple and accurate approach with machine learning. In: International Conference on Intelligent Systems Design and Applications, ISDA, pp. 714\u2013719. IEEE (2011). https:\/\/doi.org\/10.1109\/ISDA.2011.6121740","DOI":"10.1109\/ISDA.2011.6121740"},{"key":"21_CR9","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/J.NEUCOM.2014.01.057","volume":"142","author":"X Zhang","year":"2014","unstructured":"Zhang, X., Hu, Y., Xie, K., Wang, S., Ngai, E.W.T., Liu, M.: A causal feature selection algorithm for stock prediction modeling. Neurocomputing 142, 48\u201359 (2014). https:\/\/doi.org\/10.1016\/J.NEUCOM.2014.01.057","journal-title":"Neurocomputing"},{"key":"21_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/b98835","volume-title":"Principal Component Analysis","author":"IT Jolliffe","year":"2002","unstructured":"Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002). https:\/\/doi.org\/10.1007\/b98835"},{"key":"21_CR11","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/978-3-662-12405-5_15","volume-title":"Machine Learning","author":"JR Quinlan","year":"1983","unstructured":"Quinlan, J.R.: Learning efficient classification procedures and their application to chess end games. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning, pp. 463\u2013482. Springer, Heidelberg (1983). https:\/\/doi.org\/10.1007\/978-3-662-12405-5_15"},{"key":"21_CR12","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B. 58, 267\u2013288 (1996). https:\/\/doi.org\/10.1111\/j.2517-6161.1996.tb02080.x","journal-title":"J. R. Stat. Soc. Ser. B."},{"key":"21_CR13","unstructured":"TA-Lib\u202f: Technical Analysis Library. http:\/\/ta-lib.org\/"},{"key":"21_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436\u2013444 (2015). https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"21_CR15","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2016, pp. 785\u2013794. ACM Press, New York (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"21_CR16","unstructured":"McCue, T., Carruthers, E., Dawe, J., Liu, S.: Evaluation of generalized linear model assumptions using randomization (2008). http:\/\/www.mun.ca\/biology\/dschneider\/b7932\/B7932Final10Dec2008.pdf"}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33617-2_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T15:29:26Z","timestamp":1709825366000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33617-2_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030336165","9783030336172"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33617-2_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"18 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.confercare.manchester.ac.uk\/events\/ideal2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"149","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":"94","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":"0","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":"63% - 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":"2.5","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":"3","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)"}}]}}