{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:32:24Z","timestamp":1757622744551,"version":"3.44.0"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030865160"},{"type":"electronic","value":"9783030865177"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86517-7_3","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T10:08:05Z","timestamp":1631182085000},"page":"36-51","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge"],"prefix":"10.1007","author":[{"given":"Zhen","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Wei","family":"Tu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isabelle","family":"Guyon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"issue":"116","key":"3_CR1","first-page":"1","volume":"21","author":"A Alexandrov","year":"2020","unstructured":"Alexandrov, A., et al.: GluonTS: probabilistic and neural time series modeling in Python. J. Mach. Learn. Res. 21(116), 1\u20136 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR2","unstructured":"Erickson, N., et al.: AutoGluon-tabular: robust and accurate AutoML for structured data (2020)"},{"key":"3_CR3","doi-asserted-by":"publisher","unstructured":"Hutter, F., Kotthoff, L., Vanschoren, J. (eds.): Automated Machine Learning. Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-05318-5","DOI":"10.1007\/978-3-030-05318-5"},{"key":"3_CR4","unstructured":"Hyndman, R.J., Athanasopoulos, G. (eds.): Forecasting: principles and practice. OTexts (2021). https:\/\/otexts.com\/fpp3\/. Accessed 25 Mar 2021"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Jin, H., Song, Q., Hu, X.: Auto-Keras: an efficient neural architecture search system. In: KDD (2019)","DOI":"10.1145\/3292500.3330648"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Kanter, J.M., Veeramachaneni, K.: Deep feature synthesis: towards automating data science endeavors. In: IEEE International Conference on Data Science and Advanced Analytics, DSAA (2015)","DOI":"10.1109\/DSAA.2015.7344858"},{"key":"3_CR7","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems (2017)"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W., Yang, Y., Liu, H.: Modeling long- and short-term temporal patterns with deep neural networks. In: SIGIR (2018)","DOI":"10.1145\/3209978.3210006"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Lim, B., Zohren, S.: Time series forecasting with deep learning: a survey (2020)","DOI":"10.1098\/rsta.2020.0209"},{"key":"3_CR10","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.patrec.2020.04.030","volume":"135","author":"Z Liu","year":"2020","unstructured":"Liu, Z., et al.: Towards automated computer vision: analysis of the AutoCV challenges 2019. Pattern Recogn. Lett. 135, 196\u2013203 (2020)","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"3_CR11","doi-asserted-by":"publisher","first-page":"1032","DOI":"10.1007\/s10618-021-00745-9","volume":"35","author":"CW Tan","year":"2021","unstructured":"Tan, C.W., Bergmeir, C., Petitjean, F., Webb, G.I.: Time series extrinsic regression. Data Min. Knowl. Disc. 35(3), 1032\u20131060 (2021). https:\/\/doi.org\/10.1007\/s10618-021-00745-9","journal-title":"Data Min. Knowl. Disc."},{"key":"3_CR12","first-page":"e3190v2","volume":"5","author":"SJ Taylor","year":"2017","unstructured":"Taylor, S.J., Letham, B.: Forecasting at scale. PeerJ Prepr. 5, e3190v2 (2017)","journal-title":"PeerJ Prepr."},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, J., Marathe, M.: DEFSI: deep learning based epidemic forecasting with synthetic information. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33019607"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: International Joint Conference on Neural Networks (2017)","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"3_CR15","unstructured":"Yao, Q., et al.: Taking human out of learning applications: a survey on automated machine learning (2018)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86517-7_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:01:56Z","timestamp":1757368916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86517-7_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865160","9783030865177"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86517-7_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","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":"869","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":"210","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":"24% - 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":"3-4","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":"3-9","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)"}},{"value":"The conference was held online 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)"}}]}}