{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T16:50:52Z","timestamp":1771606252274,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031770654","type":"print"},{"value":"9783031770661","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-77066-1_5","type":"book-chapter","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T15:47:00Z","timestamp":1735660020000},"page":"80-95","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Highly Scalable Time Series Classification for\u00a0Very Large Datasets"],"prefix":"10.1007","author":[{"given":"Angus","family":"Dempster","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang Wei","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lynn","family":"Miller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Navid Mohammadi","family":"Foumani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel F.","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geoffrey I.","family":"Webb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,1]]},"reference":[{"key":"5_CR1","unstructured":"Bagnall, A., et al.: The UEA multivariate time series classification archive. arXiv:1811.00075 (2018)"},{"issue":"3","key":"5_CR2","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","volume":"31","author":"A Bagnall","year":"2017","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 (2017)","journal-title":"Data Min. Knowl. Disc."},{"key":"5_CR3","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1007\/3-540-45681-3_6","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"D Brain","year":"2002","unstructured":"Brain, D., Webb, G.I.: The need for low bias algorithms in classification learning from large data sets. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) Principles of Data Mining and Knowledge Discovery, pp. 62\u201373. Springer, Berlin (2002)"},{"issue":"1","key":"5_CR4","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1023\/A:1007563306331","volume":"36","author":"L Breiman","year":"1999","unstructured":"Breiman, L.: Pasting small votes for classification in large databases and on-line. Mach. Learn. 36(1), 85\u2013103 (1999)","journal-title":"Mach. Learn."},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Cabello, N., Naghizade, E., Qi, J., Kulik, L.: Fast, accurate and explainable time series classification through randomization. Data Min. Knowl. Discov. (2023)","DOI":"10.1007\/s10618-023-00978-w"},{"key":"5_CR6","unstructured":"City of Melbourne: Pedestrian counting system (2022). https:\/\/data.melbourne.vic.gov.au\/explore\/dataset\/pedestrian-counting-system-monthly-counts-per-hour\/information\/. CC BY 4.0"},{"issue":"6","key":"5_CR7","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1109\/JAS.2019.1911747","volume":"6","author":"HA Dau","year":"2019","unstructured":"Dau, H.A., et al.: The UCR time series archive. IEEE\/CAA J. Autom. Sinica 6(6), 1293\u20131305 (2019)","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"issue":"5","key":"5_CR8","doi-asserted-by":"publisher","first-page":"1454","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(5), 1454\u20131495 (2020)","journal-title":"Data Min. Knowl. Disc."},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Dempster, A., Schmidt, D.F., Webb, G.I.: MiniRocket: a very fast (almost) deterministic transform for time series classification. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 248\u2013257. ACM, New York (2021)","DOI":"10.1145\/3447548.3467231"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Dempster, A., Schmidt, D.F., Webb, G.I.: Hydra: competing convolutional kernels for fast and accurate time series classification. Data Min. Knowl. Discov. (2023)","DOI":"10.1007\/s10618-023-00939-3"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Dempster, A., Schmidt, D.F., Webb, G.I.: Quant: a minimalist interval method for time series classification. Data Min. Knowl. Discov. (2024)","DOI":"10.1007\/s10618-024-01036-9"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Fanioudakis, E., Geismar, M., Potamitis, I.: Mosquito wingbeat analysis and classification using deep learning. In: 26th European Signal Processing Conference, pp. 2410\u20132414 (2018)","DOI":"10.23919\/EUSIPCO.2018.8553542"},{"key":"5_CR13","unstructured":"Garnot, V.S.F., Landrieu, L., Giordano, S., Chehata, N.: Satellite image time series classification with pixel-set encoders and temporal self-attention (2020)"},{"key":"5_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2009)"},{"issue":"12","key":"5_CR15","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1145\/3467017","volume":"64","author":"S Hooker","year":"2021","unstructured":"Hooker, S.: The hardware lottery. Commun. ACM 64(12), 58\u201365 (2021)","journal-title":"Commun. ACM"},{"key":"5_CR16","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of Machine Learning Research, pp. 448\u2013456 (2015)"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Ismail-Fawaz, A., Devanne, M., Weber, J., Forestier, G.: Deep learning for time series classification using new hand-crafted convolution filters. In: IEEE International Conference on Big Data, pp. 972\u2013981 (2022)","DOI":"10.1109\/BigData55660.2022.10020496"},{"issue":"6","key":"5_CR18","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","volume":"34","author":"H Ismail Fawaz","year":"2020","unstructured":"Ismail Fawaz, H., et al.: InceptionTime: finding AlexNet for time series classification. Data Min. Knowl. Disc. 34(6), 1936\u20131962 (2020). https:\/\/doi.org\/10.1007\/s10618-020-00710-y","journal-title":"Data Min. Knowl. Disc."},{"key":"5_CR19","unstructured":"Louppe, G.: Understanding random forests: from theory to practice. Ph.D. thesis, University of Li\u00e8ge (2014). arXiv:2305.11921"},{"key":"5_CR20","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-642-33460-3_28","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"G Louppe","year":"2012","unstructured":"Louppe, G., Geurts, P.: Ensembles on random patches. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) Machine Learning and Knowledge Discovery in Databases, pp. 346\u2013361. Springer, Berlin (2012)"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Middlehurst, M., Large, J., Bagnall, A.: The canonical interval forest (CIF) classifier for time series classification. In: IEEE International Conference on Big Data, pp. 188\u2013195 (2020)","DOI":"10.1109\/BigData50022.2020.9378424"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Middlehurst, M., Large, J., Flynn, M., Lines, J., Bostrom, A., Bagnall, A.: HIVE-COTE 2.0: a new meta ensemble for time series classification. Mach. Learn. 110, 3211\u20133243 (2021)","DOI":"10.1007\/s10994-021-06057-9"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Middlehurst, M., Sch\u00e4fer, P., Bagnall, A.: Bake off redux: a review and experimental evaluation of recent time series classification algorithms. Data Min. Knowl. Discov. (2024)","DOI":"10.1007\/s10618-024-01022-1"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Miller, B.S., et al.: An open access dataset for developing automated detectors of Antarctic baleen whale sounds and performance evaluation of two commonly used detectors. Sci. Rep. 11 (2021)","DOI":"10.1038\/s41598-020-78995-8"},{"key":"5_CR25","unstructured":"Miller, B.S., Stafford, K.M., Van\u00a0Opzeeland, I., et\u00a0al.: Whale sounds (2020). https:\/\/data.aad.gov.au\/metadata\/AcousticTrends_BlueFinLibrary. CC BY 4.0"},{"key":"5_CR26","unstructured":"Sainte Fare\u00a0Garnot, V., Landrieu, L.: S2Agri pixel set (2022). https:\/\/zenodo.org\/records\/5815488. CC BY 4.0"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer, P., Leser, U.: WEASEL 2.0: a random dilated dictionary transform for fast, accurate and memory constrained time series classification. Mach. Learn. 112(12), 4763\u20134788 (2023)","DOI":"10.1007\/s10994-023-06395-w"},{"key":"5_CR28","unstructured":"Sutton, R.: The bitter lesson (2019). http:\/\/www.incompleteideas.net\/IncIdeas\/BitterLesson.html"},{"issue":"5","key":"5_CR29","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1007\/s10618-022-00844-1","volume":"36","author":"CW Tan","year":"2022","unstructured":"Tan, C.W., Dempster, A., Bergmeir, C., Webb, G.I.: MultiRocket: multiple pooling operators and transformations for fast and effective time series classification. Data Min. Knowl. Disc. 36(5), 1623\u20131646 (2022)","journal-title":"Data Min. Knowl. Disc."},{"key":"5_CR30","unstructured":"Tew, S., Boley, M., Schmidt, D.F.: Bayes beats cross validation: efficient and accurate ridge regression via expectation maximization. In: 37th Conference on Neural Information Processing Systems (2023)"},{"key":"5_CR31","unstructured":"The aeon Developers: aeon (2024). https:\/\/github.com\/aeon-toolkit\/aeon"},{"key":"5_CR32","unstructured":"Transport for NSW: NSW road traffic volume counts hourly (2023). https:\/\/opendata.dev.transport.nsw.gov.au\/dataset\/nsw-roads-traffic-volume-counts-api\/resource\/bca06c7e-30be-4a90-bc8b-c67428c0823a. CC BY 4.0"}],"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\/978-3-031-77066-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T16:03:17Z","timestamp":1735660997000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77066-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031770654","9783031770661"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77066-1_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 January 2025","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":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aaltd2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecml-aaltd.github.io\/aaltd2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}