{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T05:43:53Z","timestamp":1771566233518,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032155344","type":"print"},{"value":"9783032155351","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-15535-1_6","type":"book-chapter","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T04:51:56Z","timestamp":1771563116000},"page":"87-102","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Deep Dive into Alternatives to\u00a0the\u00a0Global Average Pooling for\u00a0Time Series Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7262-999X","authenticated-orcid":false,"given":"Cyril","family":"Meyer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5385-3339","authenticated-orcid":false,"given":"Ali","family":"Ismail-Fawaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1458-3855","authenticated-orcid":false,"given":"Maxime","family":"Devanne","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3694-4703","authenticated-orcid":false,"given":"Jonathan","family":"Weber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4960-7554","authenticated-orcid":false,"given":"Germain","family":"Forestier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","unstructured":"Bagnall, A., et al.: The UEA multivariate time series classification archive, 2018 (2018). https:\/\/doi.org\/10.48550\/arXiv.1811.00075","DOI":"10.48550\/arXiv.1811.00075"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Bengio, Y., L\u00e9onard, N., Courville, A.: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation (2013). https:\/\/doi.org\/10.48550\/arXiv.1308.3432","DOI":"10.48550\/arXiv.1308.3432"},{"key":"6_CR3","doi-asserted-by":"publisher","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724\u20131734. Association for Computational Linguistics, Doha, Qatar (2014). https:\/\/doi.org\/10.3115\/v1\/D14-1179","DOI":"10.3115\/v1\/D14-1179"},{"issue":"6","key":"6_CR4","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. Automatica Sinica 6(6), 1293\u20131305 (2019). https:\/\/doi.org\/10.1109\/JAS.2019.1911747","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"6_CR5","unstructured":"Dau, H.A., et al.: The UCR time series classification archive (2018)"},{"issue":"5","key":"6_CR6","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). https:\/\/doi.org\/10.1007\/s10618-020-00701-z","journal-title":"Data Min. Knowl. Disc."},{"key":"6_CR7","doi-asserted-by":"publisher","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 & Data Mining, KDD 2021, pp. 248\u2013257. Association for Computing Machinery, New York (2021). https:\/\/doi.org\/10.1145\/3447548.3467231","DOI":"10.1145\/3447548.3467231"},{"key":"6_CR8","doi-asserted-by":"publisher","unstructured":"Dempster, A., Tan, C.W., Miller, L., Foumani, N.M., Schmidt, D.F., Webb, G.I.: Highly scalable time series classification for\u00a0very large datasets. In: Lemaire, V., et al. (eds.) Advanced Analytics and Learning on Temporal Data, pp. 80\u201395. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-77066-1_5","DOI":"10.1007\/978-3-031-77066-1_5"},{"key":"6_CR9","unstructured":"Ismail-Fawaz, A., et al.: An approach to multiple comparison benchmark evaluations that is stable under manipulation of the comparate set. arXiv preprint arXiv:2305.11921 (2023)"},{"key":"6_CR10","doi-asserted-by":"publisher","unstructured":"Ismail-Fawaz, A., Devanne, M., Berretti, S., Weber, J., Forestier, G.: LITE: light inception with boosting techniques for time series classification. In: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1\u201310 (2023). https:\/\/doi.org\/10.1109\/DSAA60987.2023.10302569","DOI":"10.1109\/DSAA60987.2023.10302569"},{"key":"6_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-024-00708-5","author":"A Ismail-Fawaz","year":"2025","unstructured":"Ismail-Fawaz, A., Devanne, M., Berretti, S., Weber, J., Forestier, G.: Look into the LITE in deep learning for time series classification. Int. J. Data Sci. Anal. (2025). https:\/\/doi.org\/10.1007\/s41060-024-00708-5","journal-title":"Int. J. Data Sci. Anal."},{"issue":"4","key":"6_CR12","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"H Ismail Fawaz","year":"2019","unstructured":"Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917\u2013963 (2019). https:\/\/doi.org\/10.1007\/s10618-019-00619-1","journal-title":"Data Min. Knowl. Disc."},{"issue":"6","key":"6_CR13","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":"6_CR14","doi-asserted-by":"publisher","unstructured":"Lee, D., Lee, S., Yu, H.: Learnable dynamic temporal pooling for time series classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 9, pp. 8288\u20138296 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i9.17008","DOI":"10.1609\/aaai.v35i9.17008"},{"key":"6_CR15","unstructured":"Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)"},{"key":"6_CR16","doi-asserted-by":"publisher","unstructured":"Middlehurst, M., Bagnall, A.: Extracting features from\u00a0random subseries: a hybrid pipeline for\u00a0time series classification and\u00a0extrinsic regression. In: Ifrim, G., et al. (eds.) Advanced Analytics and Learning on Temporal Data, pp. 113\u2013126. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-49896-1_8","DOI":"10.1007\/978-3-031-49896-1_8"},{"issue":"4","key":"6_CR17","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1007\/s10618-024-01022-1","volume":"38","author":"M Middlehurst","year":"2024","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. Disc. 38(4), 1958\u20132031 (2024). https:\/\/doi.org\/10.1007\/s10618-024-01022-1","journal-title":"Data Min. Knowl. Disc."},{"issue":"5","key":"6_CR18","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). https:\/\/doi.org\/10.1007\/s10618-022-00844-1","journal-title":"Data Min. Knowl. Disc."},{"key":"6_CR19","doi-asserted-by":"publisher","unstructured":"Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578\u20131585 (2017). https:\/\/doi.org\/10.1109\/IJCNN.2017.7966039","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"6_CR20","doi-asserted-by":"publisher","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921\u20132929. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.319","DOI":"10.1109\/CVPR.2016.319"}],"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\/978-3-032-15535-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T04:51:57Z","timestamp":1771563117000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15535-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032155344","9783032155351"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15535-1_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"21 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aaltd2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecml-aaltd.github.io\/aaltd2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}