{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T12:32:08Z","timestamp":1771677128229,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"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_32","type":"book-chapter","created":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T16:03:54Z","timestamp":1604505834000},"page":"470-485","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Methods for Forecasting Nonlinear Non-stationary Processes in Machine Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7421-3565","authenticated-orcid":false,"given":"Peter","family":"Bidyuk","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3517-580X","authenticated-orcid":false,"given":"Aleksandr","family":"Gozhyj","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8359-2045","authenticated-orcid":false,"given":"Irina","family":"Kalinina","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6417-3689","authenticated-orcid":false,"given":"Victoria","family":"Vysotska","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,5]]},"reference":[{"issue":"5","key":"32_CR1","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/S0927-5398(99)00013-4","volume":"6","author":"TT Andersen","year":"1999","unstructured":"Andersen, T.T., Bollerslev, T., Lange, S.: Forecasting financial market volatility: sample frequency visa-vis forecast horizon. J. Empirical Finance 6(5), 457\u2013477 (1999)","journal-title":"J. Empirical Finance"},{"key":"32_CR2","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.jeconom.2008.12.012","volume":"150","author":"M Asai","year":"2009","unstructured":"Asai, M., McAleer, M.: The structure of dynamic correlations in multivariate stochastic volatility models. J. Econometrics 150, 182\u2013192 (2009)","journal-title":"J. Econometrics"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Bidyuk, P., Gozhyj, A., Kalinina, I.: The methods bayesian analysis of the threshold stochastic volatility model. In: Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, pp. 70\u201375 (2018)","DOI":"10.1109\/DSMP.2018.8478474"},{"key":"32_CR4","doi-asserted-by":"publisher","unstructured":"Bidyuk, P., Gozhyj, A., Kalinina, I., Gozhyj, V.: Analysis of uncertainty types for model building and forecasting dynamic processes. In: Advances in Intelligent Systems and Computing II, vol. 689, pp. 66\u201378. Springer-Verlag (2018). https:\/\/doi.org\/10.1007\/978-3-319-70581-1","DOI":"10.1007\/978-3-319-70581-1"},{"key":"32_CR5","volume-title":"Time Series Analysis","author":"PI Bidyuk","year":"2013","unstructured":"Bidyuk, P.I., Romanenko, V.D., Timoshchuk, O.L.: Time Series Analysis. Polytechnika NTU Igor Sikorsky KPI, Kyiv (2013)"},{"issue":"3","key":"32_CR6","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/0304-4076(86)90063-1","volume":"31","author":"T Bollerslev","year":"1986","unstructured":"Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econometrics 31(3), 307\u2013327 (1986)","journal-title":"J. Econometrics"},{"issue":"3","key":"32_CR7","doi-asserted-by":"publisher","first-page":"542","DOI":"10.2307\/1925546","volume":"69","author":"T Bollerslev","year":"1987","unstructured":"Bollerslev, T.: A conditionally heteroskedastic time series model forspeculative prices and rates of return. Rev. Econ. Stat. 69(3), 542\u2013547 (1987)","journal-title":"Rev. Econ. Stat."},{"issue":"1\u20132","key":"32_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/0304-4076(92)90064-X","volume":"52","author":"T Bollerslev","year":"1992","unstructured":"Bollerslev, T., Chou, R., Kroner, K.: Arch modeling in finance : a review of the theory and empirical evidence. J. Econometrics 52(1\u20132), 5\u201359 (1992). https:\/\/doi.org\/10.1016\/0304-4076(92)90064-X","journal-title":"J. Econometrics"},{"key":"32_CR9","doi-asserted-by":"publisher","first-page":"987","DOI":"10.2307\/1912773","volume":"50","author":"R Engle","year":"1982","unstructured":"Engle, R.: Autoregressive conditional heteroscedasticity with estimates of variance of United Kingdom infiation. Econometrica 50, 987\u20131008 (1982)","journal-title":"Econometrica"},{"issue":"1","key":"32_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/07474938608800095","volume":"5","author":"R Engle","year":"1986","unstructured":"Engle, R., Bollerslev, T.: Modelling the persistence of conditional variances. Econometric Rev. 5(1), 1\u201350 (1986)","journal-title":"Econometric Rev."},{"key":"32_CR11","doi-asserted-by":"publisher","unstructured":"Fulcher, B.D., Jones, N.S.: Highly comparative feature-based time-series classification. In: IEEE Transactions on Knowledge and Data Engineering, vol. 26, pp. 3026\u20133037 (2014). https:\/\/doi.org\/10.1109\/TKDE.2014.2316504","DOI":"10.1109\/TKDE.2014.2316504"},{"issue":"83","key":"32_CR12","doi-asserted-by":"publisher","first-page":"20130048","DOI":"10.1098\/rsif.2013.0048","volume":"10","author":"BD Fulcher","year":"2013","unstructured":"Fulcher, B.D., Little, M.A., Jones, N.S.: Highly comparative time-series analysis: the empirical structure of time series and their methods. J. Royal Soc. Interface 10(83), 20130048 (2013). https:\/\/doi.org\/10.1098\/rsif.2013.0048","journal-title":"J. Royal Soc. Interface"},{"key":"32_CR13","volume-title":"Econometric Analysis of Time Series","author":"AC Harvey","year":"1990","unstructured":"Harvey, A.C.: Econometric Analysis of Time Series. John Wiley and Sons Ltd, New York (1990)"},{"key":"32_CR14","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107049994","volume-title":"Forecasting, Structural Time Series Models and The Kalman Filter","author":"AC Harvey","year":"1990","unstructured":"Harvey, A.C.: Forecasting, Structural Time Series Models and The Kalman Filter. The MIT Press, Cambridge (Massachusetts) (1990)"},{"key":"32_CR15","doi-asserted-by":"publisher","unstructured":"Hyndman, R.J., Wang, E., Laptev, N.: Large-scale unusual time series detection. In: Proceedings of 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, pp. 1616\u20131619 (2016). https:\/\/doi.org\/10.1109\/ICDMW.2015.104","DOI":"10.1109\/ICDMW.2015.104"},{"issue":"3","key":"32_CR16","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.jeconom.2013.04.002","volume":"177","author":"MH Pesaran","year":"2013","unstructured":"Pesaran, M.H., Pick, A.: Optimal forecasts in the presence of structural breaks. J. Econometrics 177(3), 134\u2013152 (2013). https:\/\/doi.org\/10.1016\/j.jeconom.2013.04.002","journal-title":"J. Econometrics"},{"issue":"3","key":"32_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v086.i03","volume":"86","author":"F Pretis","year":"2018","unstructured":"Pretis, F., Reade, J.J., Succarat, G.: Automated general-to-specific (GETS) regression modeling and indicator saturation for outliers and structural breaks. Stat. Softw. 86(3), 1\u201344 (2018). https:\/\/doi.org\/10.18637\/jss.v086.i03","journal-title":"Stat. Softw."},{"key":"32_CR18","volume-title":"Gaussian Processes for Machine Learning","author":"CE Rasmussen","year":"2006","unstructured":"Rasmussen, C.E., Williams, C.I.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (Massachusetts) (2006)"},{"issue":"5","key":"32_CR19","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1111\/j.1468-0084.2011.00669.x","volume":"74","author":"G Succarat","year":"2012","unstructured":"Succarat, G., Escribano, A.: Automated model selection in finance: general-to-specific modeling of the mean, variance and density. Oxford Bull. Econ. Stat. 74(5), 716\u2013735 (2012). https:\/\/doi.org\/10.1111\/j.1468-0084.2011.00669.x","journal-title":"Oxford Bull. Econ. Stat."},{"key":"32_CR20","first-page":"2919","volume":"4","author":"T Terasvirta","year":"1994","unstructured":"Terasvirta, T., Tjostheim, D., Granger, C.J.: Aspects of modeling nonlinear time series. Handb. Econometrics 4, 2919\u20132957 (1994)","journal-title":"Handb. Econometrics"},{"key":"32_CR21","volume-title":"Quantitative Methods in Finance","author":"TJ Watsham","year":"1997","unstructured":"Watsham, T.J., Parramore, K.: Quantitative Methods in Finance. International Thomson Business Press, London (1997)"},{"key":"32_CR22","doi-asserted-by":"publisher","DOI":"10.1002\/9780470688014","volume-title":"ARCH Models for Financial Applications","author":"E Xekalaki","year":"2010","unstructured":"Xekalaki, E., Degiannakis, S.: ARCH Models for Financial Applications. The MIT Press, Cambridge (Massachusetts) (2010)"}],"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_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T18:21:24Z","timestamp":1619288484000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61656-4_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030616557","9783030616564"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61656-4_32","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)"}}]}}