{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T18:12:14Z","timestamp":1765995134232,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"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_4","type":"book-chapter","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T06:03:02Z","timestamp":1679292182000},"page":"50-65","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Fast Time Series Classification with\u00a0Random Symbolic Subsequences"],"prefix":"10.1007","author":[{"given":"Thach Le","family":"Nguyen","sequence":"first","affiliation":[]},{"given":"Georgiana","family":"Ifrim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,4]]},"reference":[{"key":"4_CR1","unstructured":"Bagnall, A., et al.: The UEA multivariate time series classification archive. arXiv preprint arXiv:1811.00075 (2018)"},{"key":"4_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-65742-0_1","volume-title":"Advanced Analytics and Learning on Temporal Data","author":"A Bagnall","year":"2020","unstructured":"Bagnall, A., Flynn, M., Large, J., Lines, J., Middlehurst, M.: On the usage and performance of the hierarchical vote collective of transformation-based ensembles version 1.0 (HIVE-COTE v1.0). In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds.) AALTD 2020. LNCS (LNAI), vol. 12588, pp. 3\u201318. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-65742-0_1"},{"issue":"3","key":"4_CR3","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":"4_CR4","unstructured":"Benavoli, A., Corani, G., Mangili, F.: Should we really use post-hoc tests based on mean-ranks? J. Mach. Learn. Res. 17(5), 1\u201310 (2016). http:\/\/jmlr.org\/papers\/v17\/benavoli16a.html"},{"issue":"1","key":"4_CR5","doi-asserted-by":"publisher","first-page":"248","DOI":"10.32614\/RJ-2016-017","volume":"8","author":"B Calvo","year":"2016","unstructured":"Calvo, B., Santaf\u00e9, G.: scmamp: statistical comparison of multiple algorithms in multiple problems. R J. 8(1), 248\u2013256 (2016). https:\/\/doi.org\/10.32614\/RJ-2016-017","journal-title":"R J."},{"issue":"5","key":"4_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":"4_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: Zhu, F., Ooi, B.C., Miao, C. (eds.) KDD 2021: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, 14\u201318 August 2021, pp. 248\u2013257. ACM (2021). https:\/\/doi.org\/10.1145\/3447548.3467231","DOI":"10.1145\/3447548.3467231"},{"key":"4_CR8","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006). http:\/\/dl.acm.org\/citation.cfm?id=1248547.1248548"},{"key":"4_CR9","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871\u20131874 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"4_CR10","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":"4_CR11","doi-asserted-by":"crossref","unstructured":"Frigo, M., Johnson, S.G.: The design and implementation of FFTW3. Proc. IEEE 93(2), 216\u2013231 (2005). Special issue on \u201cProgram Generation, Optimization, and Platform Adaptation\u201d","DOI":"10.1109\/JPROC.2004.840301"},{"key":"4_CR12","unstructured":"Frigo, M., Johnson, S.G.: Fastest Fourier transform in the west (2021). https:\/\/www.fftw.org"},{"key":"4_CR13","first-page":"2677","volume":"9","author":"S Garcia","year":"2008","unstructured":"Garcia, S., Herrera, F.: An extension on \u201cstatistical comparisons of classifiers over multiple data sets\u2019\u2019 for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677\u20132694 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR14","doi-asserted-by":"publisher","unstructured":"Grabocka, J., Schilling, N., Wistuba, M., Schmidt-Thieme, L.: Learning time-series shapelets. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 392\u2013401. ACM, New York (2014). https:\/\/doi.org\/10.1145\/2623330.2623613, http:\/\/doi.acm.org\/10.1145\/2623330.2623613","DOI":"10.1145\/2623330.2623613"},{"key":"4_CR15","doi-asserted-by":"publisher","unstructured":"Ifrim, G., Wiuf, C.: Bounded coordinate-descent for biological sequence classification in high dimensional predictor space. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 708\u2013716. ACM, New York (2011). https:\/\/doi.org\/10.1145\/2020408.2020519, http:\/\/doi.acm.org\/10.1145\/2020408.2020519","DOI":"10.1145\/2020408.2020519"},{"issue":"4","key":"4_CR16","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":"4","key":"4_CR17","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."},{"key":"4_CR18","doi-asserted-by":"publisher","unstructured":"Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2), 107\u2013144 (2007). https:\/\/doi.org\/10.1007\/s10618-007-0064-z","DOI":"10.1007\/s10618-007-0064-z"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Lin, J., Khade, R., Li, Y.: Rotation-invariant similarity in time series using bag-of-patterns representation. J. Intell. Inf. Syst. 39(2), 287\u2013315 (2012). https:\/\/doi.org\/10.1007\/s10844-012-0196-5","DOI":"10.1007\/s10844-012-0196-5"},{"key":"4_CR20","doi-asserted-by":"publisher","unstructured":"Lines, J., Taylor, S., Bagnall, A.: HIVE-COTE: the hierarchical vote collective of transformation-based ensembles for time series classification. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1041\u20131046 (2016). https:\/\/doi.org\/10.1109\/ICDM.2016.0133","DOI":"10.1109\/ICDM.2016.0133"},{"issue":"11","key":"4_CR21","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.1007\/s10994-021-06057-9","volume":"110","author":"M Middlehurst","year":"2021","unstructured":"Middlehurst, M., Large, J., Flynn, M., Lines, J., Bostrom, A., Bagnall, A.J.: HIVE-COTE 2.0: a new meta ensemble for time series classification. Mach. Learn. 110(11), 3211\u20133243 (2021). https:\/\/doi.org\/10.1007\/s10994-021-06057-9","journal-title":"Mach. Learn."},{"key":"4_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-030-33607-3_2","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2019","author":"M Middlehurst","year":"2019","unstructured":"Middlehurst, M., Vickers, W., Bagnall, A.: Scalable dictionary classifiers for time series classification. In: Yin, H., Camacho, D., Tino, P., Tall\u00f3n-Ballesteros, A.J., Menezes, R., Allmendinger, R. (eds.) IDEAL 2019. LNCS, vol. 11871, pp. 11\u201319. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33607-3_2"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., Keogh, E.: Fast shapelets: a scalable algorithm for discovering time series shapelets. In: Proceedings of the thirteenth SIAM conference on data mining (SDM), pp. 668\u2013676. SIAM (2013)","DOI":"10.1137\/1.9781611972832.74"},{"issue":"6","key":"4_CR24","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1007\/s10618-014-0377-7","volume":"29","author":"P Sch\u00e4fer","year":"2015","unstructured":"Sch\u00e4fer, P.: The boss is concerned with time series classification in the presence of noise. Data Min. Knowl. Disc. 29(6), 1505\u20131530 (2015)","journal-title":"Data Min. Knowl. Disc."},{"issue":"5","key":"4_CR25","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1007\/s10618-015-0441-y","volume":"30","author":"P Sch\u00e4fer","year":"2015","unstructured":"Sch\u00e4fer, P.: Scalable time series classification. Data Min. Knowl. Disc. 30(5), 1273\u20131298 (2015). https:\/\/doi.org\/10.1007\/s10618-015-0441-y","journal-title":"Data Min. Knowl. Disc."},{"key":"4_CR26","doi-asserted-by":"publisher","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, EDBT 2012, pp. 516\u2013527. ACM, New York (2012). https:\/\/doi.org\/10.1145\/2247596.2247656, http:\/\/doi.acm.org\/10.1145\/2247596.2247656","DOI":"10.1145\/2247596.2247656"},{"key":"4_CR27","doi-asserted-by":"publisher","unstructured":"Sch\u00e4fer, P., Leser, U.: Fast and accurate time series classification with weasel. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, pp. 637\u2013646. ACM, New York (2017). https:\/\/doi.org\/10.1145\/3132847.3132980, http:\/\/doi.acm.org\/10.1145\/3132847.3132980","DOI":"10.1145\/3132847.3132980"},{"key":"4_CR28","doi-asserted-by":"publisher","unstructured":"Senin, P., Malinchik, S.: SAX-VSM: interpretable time series classification using sax and vector space model. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 1175\u20131180 (2013). https:\/\/doi.org\/10.1109\/ICDM.2013.52","DOI":"10.1109\/ICDM.2013.52"},{"key":"4_CR29","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1007\/s10618-020-00679-8","volume":"34","author":"A Shifaz","year":"2020","unstructured":"Shifaz, A., Pelletier, C., Petitjean, F., Webb, G.: TS-CHIEF: a scalable and accurate forest algorithm for time series classification. Data Min. Knowl. Disc. 34, 742\u2013775 (2020)","journal-title":"Data Min. Knowl. Disc."},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947\u2013956. ACM (2009)","DOI":"10.1145\/1557019.1557122"}],"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_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T06:04:25Z","timestamp":1679292265000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24378-3_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031243776","9783031243783"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24378-3_4","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)"}}]}}