{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:25:22Z","timestamp":1771064722657,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030954697","type":"print"},{"value":"9783030954703","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-95470-3_5","type":"book-chapter","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T10:07:13Z","timestamp":1643710033000},"page":"51-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Explainable AI for\u00a0Financial Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9481-511X","authenticated-orcid":false,"given":"Salvatore","family":"Carta","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7862-8362","authenticated-orcid":false,"given":"Alessandro Sebastian","family":"Podda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8646-6183","authenticated-orcid":false,"given":"Diego","family":"Reforgiato\u00a0Recupero","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6522-908X","authenticated-orcid":false,"given":"Maria Madalina","family":"Stanciu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138\u201352160 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2870052","journal-title":"IEEE Access"},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A Arrieta","year":"2020","unstructured":"Arrieta, A., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"issue":"1","key":"5_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"key":"5_CR4","doi-asserted-by":"publisher","first-page":"30193","DOI":"10.1109\/ACCESS.2021.3059960","volume":"9","author":"SM Carta","year":"2021","unstructured":"Carta, S.M., Consoli, S., Piras, L., Podda, A.S., Recupero, D.R.: Explainable machine learning exploiting news and domain-specific lexicon for stock market forecasting. IEEE Access 9, 30193\u201330205 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3059960","journal-title":"IEEE Access"},{"key":"5_CR5","doi-asserted-by":"publisher","first-page":"29942","DOI":"10.1109\/ACCESS.2021.3059187","volume":"9","author":"SM Carta","year":"2021","unstructured":"Carta, S.M., Consoli, S., Podda, A.S., Recupero, D.R., Stanciu, M.M.: Ensembling and dynamic asset selection for risk-controlled statistical arbitrage. IEEE Access 9, 29942\u201329959 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3059187","journal-title":"IEEE Access"},{"key":"5_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3390\/fi11010005","volume":"11","author":"S Carta","year":"2019","unstructured":"Carta, S., Medda, A., Pili, A., Reforgiato Recupero, D., Saia, R.: Forecasting e-commerce products prices by combining an autoregressive integrated moving average (arima) model and google trends data. Future Internet 11, 5 (2019)","journal-title":"Future Internet"},{"key":"5_CR7","unstructured":"Chen, J., Song, L., Wainwright, M.J., Jordan, M.I.: Learning to explain: an information-theoretic perspective on model interpretation. CoRR abs\/1802.07814 (2018). http:\/\/arxiv.org\/abs\/1802.07814"},{"key":"5_CR8","unstructured":"Choi, E., Bahadori, M., Kulas, J., Schuetz, A., Stewart, W., Sun, J.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. In: Advances in Neural Information Processing Systems, 30th Annual Conference on Neural Information Processing Systems, NIPS 2016, 05 December 2016 Through 10 December 2016, pp. 3512\u20133520, January 2016"},{"key":"5_CR9","doi-asserted-by":"publisher","unstructured":"Cortez, P., Embrechts, M.J.: Opening black box data mining models using sensitivity analysis. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 341\u2013348 (2011). https:\/\/doi.org\/10.1109\/CIDM.2011.5949423","DOI":"10.1109\/CIDM.2011.5949423"},{"issue":"2","key":"5_CR10","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","volume":"270","author":"T Fischer","year":"2018","unstructured":"Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res. 270(2), 654\u2013669 (2018)","journal-title":"Eur. J. Oper. Res."},{"key":"5_CR11","unstructured":"Fisher, A.J., Rudin, C., Dominici, F.: Model class reliance: variable importance measures for any machine learning model class, from the \u201crashomon\u201d perspective (2018)"},{"key":"5_CR12","doi-asserted-by":"publisher","unstructured":"Fong, R.C., Vedaldi, A.: Interpretable explanations of black boxes by meaningful perturbation. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3449\u20133457 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.371","DOI":"10.1109\/ICCV.2017.371"},{"key":"5_CR13","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.eswa.2019.01.012","volume":"124","author":"BM Henrique","year":"2019","unstructured":"Henrique, B.M., Sobreiro, V.A., Kimura, H.: Literature review: machine learning techniques applied to financial market prediction. Expert Syst. Appl. 124, 226\u2013251 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"5_CR14","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1214\/aoms\/1177730196","volume":"19","author":"W Hoeffding","year":"1948","unstructured":"Hoeffding, W.: A class of statistics with asymptotically normal distribution. Ann. Math. Stat. 19(3), 293\u2013325 (1948). https:\/\/doi.org\/10.1214\/aoms\/1177730196","journal-title":"Ann. Math. Stat."},{"issue":"1","key":"5_CR15","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/J.EJOR.2019.04.013","volume":"278","author":"N Huck","year":"2019","unstructured":"Huck, N.: Large data sets and machine learning: applications to statistical arbitrage. Eur. J. Oper. Res. 278(1), 330\u2013342 (2019). https:\/\/doi.org\/10.1016\/J.EJOR.2019.04.013","journal-title":"Eur. J. Oper. Res."},{"key":"5_CR16","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/978-3-319-47175-4_20","volume-title":"Research and Development in Intelligent Systems XXXIII","author":"JKC Kingston","year":"2016","unstructured":"Kingston, J.K.C.: Artificial intelligence and legal liability. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXXIII, pp. 269\u2013279. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-47175-4_20"},{"issue":"2","key":"5_CR17","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.ejor.2016.10.031","volume":"259","author":"C Krauss","year":"2017","unstructured":"Krauss, C., Do, X.A., Huck, N.: Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S&P 500. Eur. J. Oper. Res. 259(2), 689\u2013702 (2017)","journal-title":"Eur. J. Oper. Res."},{"key":"5_CR18","first-page":"633","volume":"165","author":"J Kroll","year":"2017","unstructured":"Kroll, J., et al.: Accountable algorithms. Univ. Pennsylvania Law Rev. 165, 633\u2013705 (2017)","journal-title":"Univ. Pennsylvania Law Rev."},{"key":"5_CR19","unstructured":"Louppe, G., Wehenkel, L., Sutera, A., Geurts, P.: Understanding variable importances in forests of randomized trees. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 431\u2013439. Curran Associates, Inc. (2013). https:\/\/proceedings.neurips.cc\/paper\/2013\/file\/e3796ae838835da0b6f6ea37bcf8bcb7-Paper.pdf"},{"issue":"10","key":"5_CR20","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","volume":"2","author":"SM Lundberg","year":"2018","unstructured":"Lundberg, S.M., et al.: Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2(10), 749\u2013760 (2018). https:\/\/doi.org\/10.1038\/s41551-018-0304-0","journal-title":"Nat. Biomed. Eng."},{"key":"5_CR21","doi-asserted-by":"publisher","unstructured":"Man, X., Chan, E.P.: The best way to select features? Comparing MDA, LIME, and SHAP. J. Financ. Data Sci. 3(1), 127\u2013139 (2020). https:\/\/doi.org\/10.3905\/jfds.2020.1.047. https:\/\/jfds.pm-research.com\/content\/early\/2020\/12\/04\/jfds.2020.1.047","DOI":"10.3905\/jfds.2020.1.047"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Molnar, C., Casalicchio, G., Bischl, B.: Interpretable machine learning-a brief history, state-of-the-art and challenges (2020)","DOI":"10.1007\/978-3-030-65965-3_28"},{"key":"5_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR24","volume-title":"Advances in Financial Machine Learning","author":"ML de Prado","year":"2018","unstructured":"de Prado, M.L.: Advances in Financial Machine Learning, 1st edn. Wiley Publishing, Hoboken (2018)","edition":"1"},{"key":"5_CR25","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should i trust you?\u201d: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 1135\u20131144. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Song, H., Rajan, D., Thiagarajan, J., Spanias, A.: Attend and diagnose: clinical time series analysis using attention models. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 4091\u20134098. AAAI Press (2018)","DOI":"10.1609\/aaai.v32i1.11635"},{"issue":"1","key":"5_CR27","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1186\/1471-2105-9-307","volume":"9","author":"C Strobl","year":"2008","unstructured":"Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC Bioinform. 9(1), 307 (2008). https:\/\/doi.org\/10.1186\/1471-2105-9-307","journal-title":"BMC Bioinform."},{"key":"5_CR28","first-page":"1","volume":"11","author":"E Strumbelj","year":"2010","unstructured":"Strumbelj, E., Kononenko, I.: An efficient explanation of individual classifications using game theory. J. Mach. Learn. Res. 11, 1\u201318 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR29","unstructured":"Suresh, H., Hunt, N., Johnson, A., Celi, L.A., Szolovits, P., Ghassemi, M.: Clinical intervention prediction and understanding with deep neural networks. In: Doshi-Velez, F., Fackler, J., Kale, D., Ranganath, R., Wallace, B., Wiens, J. (eds.) Proceedings of the 2nd Machine Learning for Healthcare Conference. Proceedings of Machine Learning Research, Boston, Massachusetts, 18\u201319 August 2017, vol. 68, pp. 322\u2013337. PMLR (2017). http:\/\/proceedings.mlr.press\/v68\/suresh17a.html"},{"key":"5_CR30","unstructured":"Tonekaboni, S., Joshi, S., Campbell, K., Duvenaud, D.K., Goldenberg, A.: What went wrong and when? Instance-wise feature importance for time-series black-box models. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 799\u2013809. Curran Associates, Inc. (2020)"},{"key":"5_CR31","unstructured":"Yoon, J., Jordon, J., van der Schaar, M.: INVASE: instance-wise variable selection using neural networks. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=BJg_roAcK7"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95470-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T11:56:40Z","timestamp":1674647800000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95470-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030954697","9783030954703"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95470-3_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grasmere","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2021.icas.cc\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"215","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":"86","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":"40% - 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":"5-6","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":"1-2","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)"}}]}}