{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T10:08:23Z","timestamp":1771236503210,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030616557","type":"print"},{"value":"9783030616564","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-61656-4_33","type":"book-chapter","created":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T16:03:54Z","timestamp":1604505834000},"page":"486-500","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9515-3488","authenticated-orcid":false,"given":"Bohdan","family":"Pavlyshenko","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,5]]},"reference":[{"key":"33_CR1","unstructured":"Rossmann Store Sales. Forecast sales using store, promotion, and competitor data. Kaggle. Com. Available online: http:\/\/www.kaggle.com\/c\/rossmann-store-sales"},{"key":"33_CR2","doi-asserted-by":"publisher","DOI":"10.1111\/jtsa.12194","volume-title":"Time Series Analysis: Forecasting and Control","author":"GE Box","year":"2015","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. John Wiley & Sons, Hoboken (2015). https:\/\/doi.org\/10.1111\/jtsa.12194"},{"key":"33_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/b97391","volume-title":"Introduction to Time Series and Forecasting","author":"PJ Brockwell","year":"2002","unstructured":"Brockwell, P.J., Davis, R.A., Calder, M.V.: Introduction to Time Series and Forecasting, vol. 2. Springer, Berlin (2002)"},{"key":"33_CR4","doi-asserted-by":"publisher","unstructured":"Carpenter, B., et al.: Stan: a probabilistic programming language. J. Stat. Softw. 76(1) (2017). https:\/\/doi.org\/10.18637\/jss.v076.i01","DOI":"10.18637\/jss.v076.i01"},{"key":"33_CR5","doi-asserted-by":"publisher","DOI":"10.1201\/9781420036206","volume-title":"Time-Series Forecasting","author":"C Chatfield","year":"2000","unstructured":"Chatfield, C.: Time-Series Forecasting. Chapman and Hall\/CRC, United Kingdom (2000). https:\/\/doi.org\/10.1201\/9781420036206"},{"key":"33_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","volume-title":"Multiple Classifier Systems","author":"TG Dietterich","year":"2000","unstructured":"Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1\u201315. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-45014-9_1"},{"issue":"3","key":"33_CR7","doi-asserted-by":"publisher","first-page":"6697","DOI":"10.1016\/j.eswa.2008.08.058","volume":"36","author":"T Efendigil","year":"2009","unstructured":"Efendigil, T., \u00d6n\u00fct, S., Kahraman, C.: A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: a comparative analysis. Expert Syst. Appl. 36(3), 6697\u20136707 (2009). https:\/\/doi.org\/10.1016\/j.eswa.2008.08.058","journal-title":"Expert Syst. Appl."},{"key":"33_CR8","doi-asserted-by":"publisher","DOI":"10.1201\/b1601","volume-title":"Bayesian Data Analysis","author":"A Gelman","year":"2013","unstructured":"Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B.: Bayesian Data Analysis. Chapman and Hall\/CRC, United Kingdom (2013). https:\/\/doi.org\/10.1201\/b1601"},{"issue":"2","key":"33_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3054925","volume":"50","author":"HM Gomes","year":"2017","unstructured":"Gomes, H.M., Barddal, J.P., Enembreck, F., Bifet, A.: A survey on ensemble learning for data stream classification. ACM Comput. Surv. (CSUR) 50(2), 1\u201336 (2017). https:\/\/doi.org\/10.1145\/3054925","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"33_CR10","volume-title":"Forecasting: Principles and Practice","author":"RJ Hyndman","year":"2018","unstructured":"Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice. OTexts, Melbourne (2018)"},{"key":"33_CR11","volume-title":"Automatic Time Series for Forecasting: The Forecast Package for R","author":"RJ Hyndman","year":"2007","unstructured":"Hyndman, R.J., Khandakar, Y., et al.: Automatic Time Series for Forecasting: The Forecast Package for R, vol. 6. Monash University, Department of Econometrics and Business Statistics, Melbourne (2007)"},{"key":"33_CR12","volume-title":"Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan","author":"J Kruschke","year":"2014","unstructured":"Kruschke, J.: Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Academic Press, United States (2014)"},{"key":"33_CR13","doi-asserted-by":"publisher","DOI":"10.4135\/9781452204444","volume-title":"Sales Forecasting Management: A Demand Management Approach","author":"JT Mentzer","year":"2005","unstructured":"Mentzer, J.T., Moon, M.A.: Sales Forecasting Management: A Demand Management Approach. Sage, United States (2005). https:\/\/doi.org\/10.4135\/9781452204444"},{"issue":"15","key":"33_CR14","doi-asserted-by":"publisher","first-page":"5207","DOI":"10.1007\/s11269-018-2155-6","volume":"32","author":"G Papacharalampous","year":"2018","unstructured":"Papacharalampous, G., Tyralis, H., Koutsoyiannis, D.: Univariate time series forecasting of temperature and precipitation with a focus on machine learning algorithms: a multiple-case study from Greece. Water Res. Manage. 32(15), 5207\u20135239 (2018). https:\/\/doi.org\/10.1007\/s11269-018-2155-6","journal-title":"Water Res. Manage."},{"key":"33_CR15","doi-asserted-by":"publisher","unstructured":"Pavlyshenko, B.: Machine learning, linear and bayesian models for logistic regression in failure detection problems. In: Big Data (Big Data), 2016 IEEE International Conference on, pp. 2046\u20132050. IEEE (2016). https:\/\/doi.org\/10.1109\/bigdata.2016.7840828","DOI":"10.1109\/bigdata.2016.7840828"},{"key":"33_CR16","doi-asserted-by":"publisher","unstructured":"Pavlyshenko, B.M.: Linear, machine learning and probabilistic approaches for time series analysis. In: Data Stream Mining & Processing (DSMP), IEEE First International Conference on, pp. 377\u2013381. IEEE (2016). https:\/\/doi.org\/10.1109\/dsmp.2016.7583582","DOI":"10.1109\/dsmp.2016.7583582"},{"issue":"1","key":"33_CR17","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3390\/data4010015","volume":"4","author":"BM Pavlyshenko","year":"2019","unstructured":"Pavlyshenko, B.M.: Machine-learning models for sales time series forecasting. Data 4(1), 15 (2019). https:\/\/doi.org\/10.3390\/data4010015","journal-title":"Data"},{"key":"33_CR18","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1007\/0-387-25465-x_45","volume-title":"Data Mining and Knowledge Discovery Handbook","author":"L Rokach","year":"2005","unstructured":"Rokach, L.: Ensemble methods for classifiers. Data Mining and Knowledge Discovery Handbook, pp. 957\u2013980. Springer, Berlin (2005). https:\/\/doi.org\/10.1007\/0-387-25465-x_45"},{"issue":"1\u20132","key":"33_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","volume":"33","author":"L Rokach","year":"2010","unstructured":"Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1\u20132), 1\u201339 (2010). https:\/\/doi.org\/10.1007\/s10462-009-9124-7","journal-title":"Artif. Intell. Rev."},{"issue":"4","key":"33_CR20","doi-asserted-by":"publisher","first-page":"e1249","DOI":"10.1002\/widm.1249","volume":"8","author":"O Sagi","year":"2018","unstructured":"Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 8(4), e1249 (2018). https:\/\/doi.org\/10.1002\/widm.1249","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Discov."},{"key":"33_CR21","doi-asserted-by":"publisher","DOI":"10.1002\/0471746193","volume-title":"Analysis of Financial Time Series","author":"RS Tsay","year":"2005","unstructured":"Tsay, R.S.: Analysis of Financial Time Series, vol. 543. John Wiley & Sons, Hoboken (2005). https:\/\/doi.org\/10.1002\/0471746193"},{"key":"33_CR22","unstructured":"Wei, W.W.: Time series analysis. In: The Oxford Handbook of Quantitative Methods in Psychology, Vol. 2 (2006)"},{"issue":"2","key":"33_CR23","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/s0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241\u2013259 (1992). https:\/\/doi.org\/10.1016\/s0893-6080(05)80023-1","journal-title":"Neural Netw."},{"key":"33_CR24","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-59140-176-6","volume-title":"Neural Networks in Business Forecasting","author":"GP Zhang","year":"2004","unstructured":"Zhang, G.P.: Neural Networks in Business Forecasting. IGI Global, United States (2004). https:\/\/doi.org\/10.4018\/978-1-59140-176-6"}],"container-title":["Communications in Computer and Information Science","Data Stream Mining & Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61656-4_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T02:37:21Z","timestamp":1619231841000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61656-4_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030616557","9783030616564"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61656-4_33","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"5 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DSMP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Stream Mining and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ukraine","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dsmp2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dsmp.in.ua\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"134","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":"36","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":"27% - 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":"7","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","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)"}}]}}