{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:02:19Z","timestamp":1765357339621,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031243776"},{"type":"electronic","value":"9783031243783"}],"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\/978-3-031-24378-3_2","type":"book-chapter","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T06:03:02Z","timestamp":1679292182000},"page":"18-33","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Clustering of\u00a0Time Series Based on\u00a0Forecasting Performance of\u00a0Global Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1456-7342","authenticated-orcid":false,"given":"\u00c1ngel","family":"L\u00f3pez-Oriona","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3816-0985","authenticated-orcid":false,"given":"Pablo","family":"Montero-Manso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5494-171X","authenticated-orcid":false,"given":"Jos\u00e9 A.","family":"Vilar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,4]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112896","volume":"140","author":"K Bandara","year":"2020","unstructured":"Bandara, K., Bergmeir, C., Smyl, S.: Forecasting across time series databases using recurrent neural networks on groups of similar series: a clustering approach. Expert Syst. Appl. 140, 112896 (2020)","journal-title":"Expert Syst. Appl."},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.fss.2016.01.010","volume":"305","author":"P D\u2019Urso","year":"2016","unstructured":"D\u2019Urso, P., De Giovanni, L., Massari, R.: Garch-based robust clustering of time series. Fuzzy Sets Syst. 305, 1\u201328 (2016)","journal-title":"Fuzzy Sets Syst."},{"issue":"1","key":"2_CR3","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1007\/s10479-019-03284-1","volume":"299","author":"P D\u2019Urso","year":"2021","unstructured":"D\u2019Urso, P., De Giovanni, L., Massari, R.: Trimmed fuzzy clustering of financial time series based on dynamic time warping. Ann. Oper. Res. 299(1), 1379\u20131395 (2021)","journal-title":"Ann. Oper. Res."},{"key":"2_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113705","volume":"161","author":"P D\u2019Urso","year":"2020","unstructured":"D\u2019Urso, P., De Giovanni, L., Massari, R., D\u2019Ecclesia, R.L., Maharaj, E.A.: Cepstral-based clustering of financial time series. Expert Syst. Appl. 161, 113705 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"24","key":"2_CR5","doi-asserted-by":"publisher","first-page":"3565","DOI":"10.1016\/j.fss.2009.04.013","volume":"160","author":"P D\u2019Urso","year":"2009","unstructured":"D\u2019Urso, P., Maharaj, E.A.: Autocorrelation-based fuzzy clustering of time series. Fuzzy Sets Syst. 160(24), 3565\u20133589 (2009)","journal-title":"Fuzzy Sets Syst."},{"issue":"1","key":"2_CR6","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193\u2013218 (1985)","journal-title":"J. Classif."},{"key":"2_CR7","unstructured":"Hyndman, R., et al.: Forecasting functions for time series and linear models. R package version 6 (2015)"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Hyndman, R.J., Wang, E., Laptev, N.: Large-scale unusual time series detection. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1616\u20131619. IEEE (2015)","DOI":"10.1109\/ICDMW.2015.104"},{"issue":"4","key":"2_CR9","first-page":"43","volume":"4","author":"RJ Hyndman","year":"2006","unstructured":"Hyndman, R.J., et al.: Another look at forecast-accuracy metrics for intermittent demand. Foresight Int. J. Appl. Forecast. 4(4), 43\u201346 (2006)","journal-title":"Foresight Int. J. Appl. Forecast."},{"issue":"11","key":"2_CR10","doi-asserted-by":"publisher","first-page":"1857","DOI":"10.1016\/j.patcog.2005.01.025","volume":"38","author":"TW Liao","year":"2005","unstructured":"Liao, T.W.: Clustering of time series data: a survey. Pattern Recogn. 38(11), 1857\u20131874 (2005)","journal-title":"Pattern Recogn."},{"issue":"2","key":"2_CR11","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1002\/for.3980010202","volume":"1","author":"S Makridakis","year":"1982","unstructured":"Makridakis, S., et al.: The accuracy of extrapolation (time series) methods: results of a forecasting competition. J. Forecast. 1(2), 111\u2013153 (1982)","journal-title":"J. Forecast."},{"issue":"4","key":"2_CR12","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.1016\/j.ijforecast.2021.03.004","volume":"37","author":"P Montero-Manso","year":"2021","unstructured":"Montero-Manso, P., Hyndman, R.J.: Principles and algorithms for forecasting groups of time series: locality and globality. Int. J. Forecast. 37(4), 1632\u20131653 (2021)","journal-title":"Int. J. Forecast."},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Paparrizos, J., Gravano, L.: K-shape: efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1855\u20131870 (2015)","DOI":"10.1145\/2723372.2737793"},{"issue":"11","key":"2_CR14","doi-asserted-by":"publisher","first-page":"2850","DOI":"10.1016\/j.csda.2009.02.015","volume":"54","author":"JA Vilar","year":"2010","unstructured":"Vilar, J.A., Alonso, A.M., Vilar, J.M.: Non-linear time series clustering based on non-parametric forecast densities. Comput. Stat. Data Anal. 54(11), 2850\u20132865 (2010)","journal-title":"Comput. Stat. Data Anal."}],"container-title":["Lecture Notes in Computer Science","Advanced Analytics and Learning on Temporal Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24378-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T06:03:46Z","timestamp":1679292226000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24378-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031243776","9783031243783"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24378-3_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"4 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AALTD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Advanced Analytics and Learning on Temporal Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","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":"aaltd2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/project.inria.fr\/aaltd22\/","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":"21","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":"12","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":"57% - 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":"2-3","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":"2-3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}