{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T06:28:49Z","timestamp":1768285729116,"version":"3.49.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030914448","type":"print"},{"value":"9783030914455","type":"electronic"}],"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\/978-3-030-91445-5_3","type":"book-chapter","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T11:05:40Z","timestamp":1638443140000},"page":"36-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Fast Channel Selection for Scalable Multivariate Time Series Classification"],"prefix":"10.1007","author":[{"given":"Bhaskar","family":"Dhariyal","sequence":"first","affiliation":[]},{"given":"Thach Le","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Georgiana","family":"Ifrim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"3_CR1","unstructured":"Consumer enthusiasm for wearable devices drives the market to 28.4% growth in 2020 (2021). https:\/\/www.idc.com\/getdoc.jsp?containerId=prUS47534521"},{"issue":"3","key":"3_CR2","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","volume":"31","author":"A Bagnall","year":"2016","unstructured":"Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31(3), 606\u2013660 (2016). https:\/\/doi.org\/10.1007\/s10618-016-0483-9","journal-title":"Data Min. Knowl. Disc."},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2019","unstructured":"Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., Sheikh, Y.A.: OpenPose: realtime multi-person 2d pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43, 172\u2013186 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10618-020-00701-z","volume":"34","author":"A Dempster","year":"2020","unstructured":"Dempster, A., Petitjean, F., Webb, G.I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Disc. 34, 1\u201342 (2020)","journal-title":"Data Min. Knowl. Disc."},{"key":"3_CR5","doi-asserted-by":"publisher","unstructured":"Dhariyal, B., Le Nguyen, T., Gsponer, S., Ifrim, G.: An examination of the state-of-the-art for multivariate time series classification. In: 2020 International Conference on Data Mining Workshops (ICDMW), pp. 243\u2013250 (2020). https:\/\/doi.org\/10.1109\/ICDMW51313.2020.00042","DOI":"10.1109\/ICDMW51313.2020.00042"},{"key":"3_CR6","unstructured":"Han, S., Niculescu-Mizil, A.: Supervised feature subset selection and feature ranking for multivariate time series without feature extraction. arXiv preprint arXiv:2005.00259 (2020)"},{"key":"3_CR7","doi-asserted-by":"publisher","unstructured":"Hu, B., Chen, Y., Zakaria, J., Ulanova, L., Keogh, E.: Classification of multi-dimensional streaming time series by weighting each classifier\u2019s track record. In: 2013 IEEE 13th International Conference on Data Mining, pp. 281\u2013290 (2013). https:\/\/doi.org\/10.1109\/ICDM.2013.33","DOI":"10.1109\/ICDM.2013.33"},{"key":"3_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/978-3-030-65742-0_15","volume-title":"Advanced Analytics and Learning on Temporal Data","author":"B Kathirgamanathan","year":"2020","unstructured":"Kathirgamanathan, B., Cunningham, P.: A feature selection method for multi-dimension time-series data. In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds.) AALTD 2020. LNCS (LNAI), vol. 12588, pp. 220\u2013231. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-65742-0_15"},{"issue":"367","key":"3_CR9","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1080\/01621459.1979.10481674","volume":"74","author":"W Krzanowski","year":"1979","unstructured":"Krzanowski, W.: Between-groups comparison of principal components. J. Am. Stat. Assoc. 74(367), 703\u2013707 (1979)","journal-title":"J. Am. Stat. Assoc."},{"issue":"4","key":"3_CR10","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1007\/s10618-019-00633-3","volume":"33","author":"T Le Nguyen","year":"2019","unstructured":"Le Nguyen, T., Gsponer, S., Ilie, I., O\u2019Reilly, M., Ifrim, G.: Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations. Data Min. Knowl. Disc. 33(4), 1183\u20131222 (2019). https:\/\/doi.org\/10.1007\/s10618-019-00633-3","journal-title":"Data Min. Knowl. Disc."},{"issue":"2","key":"3_CR11","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10618-007-0064-z","volume":"15","author":"J Lin","year":"2007","unstructured":"Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Disc. 15(2), 107\u2013144 (2007). https:\/\/doi.org\/10.1007\/s10618-007-0064-z","journal-title":"Data Min. Knowl. Disc."},{"key":"3_CR12","unstructured":"L\u00f6ning, M., Bagnall, A., Ganesh, S., Kazakov, V., Lines, J., Kir\u00e1ly, F.J.: sktime: a unified interface for machine learning with time series. arXiv preprint arXiv:1909.07872 (2019)"},{"issue":"2","key":"3_CR13","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s10618-020-00727-3","volume":"35","author":"AP Ruiz","year":"2020","unstructured":"Ruiz, A.P., Flynn, M., Large, J., Middlehurst, M., Bagnall, A.: The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 35(2), 401\u2013449 (2020). https:\/\/doi.org\/10.1007\/s10618-020-00727-3","journal-title":"Data Min. Knowl. Disc."},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Satopaa, V., Albrecht, J., Irwin, D., Raghavan, B.: Finding a \u201ckneedle\u201d in a haystack: detecting knee points in system behavior. In: 2011 31st International Conference on Distributed Computing Systems Workshops, pp. 166\u2013171. IEEE (2011)","DOI":"10.1109\/ICDCSW.2011.20"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer, P., H\u00f6gqvist, M.: SFA: a symbolic Fourier approximation and index for similarity search in high dimensional datasets. In: Proceedings of the 15th International Conference on Extending Database Technology, pp. 516\u2013527 (2012)","DOI":"10.1145\/2247596.2247656"},{"key":"3_CR16","unstructured":"Sch\u00e4fer, P., Leser, U.: Multivariate time series classification with WEASEL+ muse. In: ECML\/PKDD Workshop on Advanced Analytics and Learning on Temporal Data (AALTD 2018), arXiv preprint arXiv:1711.11343 (2017)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Shokoohi-Yekta, M., Wang, J., Keogh, E.J.: On the non-trivial generalization of dynamic time warping to the multi-dimensional case. In: SDM (2015)","DOI":"10.1137\/1.9781611974010.33"},{"key":"3_CR18","unstructured":"Singh, A., et al.: Interpretable classification of human exercise videos through pose estimation and multivariate time series analysis. In: 5th International Workshop on Health Intelligence (W3PHIAI 2021) at AAAI21. Springer (2021)"},{"issue":"9","key":"3_CR19","doi-asserted-by":"publisher","first-page":"1186","DOI":"10.1109\/TKDE.2005.144","volume":"17","author":"H Yoon","year":"2005","unstructured":"Yoon, H., Yang, K., Shahabi, C.: Feature subset selection and feature ranking for multivariate time series. IEEE Trans. Knowl. Data Eng. 17(9), 1186\u20131198 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"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\/978-3-030-91445-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T01:01:44Z","timestamp":1764637304000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91445-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030914448","9783030914455"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91445-5_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","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":"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":"13 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aaltd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/project.inria.fr\/aaltd21\/","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}