{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:18:19Z","timestamp":1779175099372,"version":"3.51.4"},"reference-count":118,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100006374","name":"HORIZON EUROPE Digital, Industry and Space","doi-asserted-by":"publisher","award":["101070122"],"award-info":[{"award-number":["101070122"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,6,17]]},"abstract":"<jats:p>Distance measures are fundamental to time series analysis and have been extensively studied for decades. Until now, research efforts mainly focused on univariate time series, leaving multivariate cases largely under-explored. Furthermore, the existing experimental studies on multivariate distances have critical limitations: (a) focusing only on lock-step and elastic measures while ignoring categories such as sliding and kernel measures; (b) considering only one normalization technique; and (c) placing limited focus on statistical analysis of findings. Motivated by these shortcomings, we present the most complete evaluation of multivariate distance measures to date. Our study examines 30 standalone measures across 8 categories, 2 channel-dependency models, and considers 13 normalizations. We perform a comprehensive evaluation across 30 datasets and 3 downstream tasks, accompanied by rigorous statistical analysis. To ensure fairness, we conduct a thorough investigation of parameters for methods in both a supervised and an unsupervised manner. Our work verifies and extends earlier findings, showing that insights from univariate distance measures also apply to the multivariate case: (a) alternative normalization methods outperform Z-score, and for the first time, we demonstrate statistical differences in certain categories for the multivariate case; (b) multiple lock-step measures are better suited than Euclidean distance, when it comes to multivariate time series; and (c) newer elastic measures outperform the widely adopted Dynamic Time Warping distance, especially with proper parameter tuning in the supervised setting. Moreover, our results reveal that (a) sliding measures offer the best trade-off between accuracy and runtime; (b) current normalization techniques fail to significantly enhance accuracy on multivariate time series and, surprisingly, do not outperform the no normalization case, indicating a lack of appropriate solutions for normalizing multivariate time series; and (c) independent consideration of time series channels is beneficial only for elastic measures. In summary, we offer guidelines to aid in designing and selecting preprocessing strategies and multivariate distance measures for our community.<\/jats:p>","DOI":"10.1145\/3725258","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:23:29Z","timestamp":1750281809000},"page":"1-29","source":"Crossref","is-referenced-by-count":14,"title":["A Structured Study of Multivariate Time-Series Distance Measures"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9069-0591","authenticated-orcid":false,"given":"Jens E.","family":"d'Hondt","sequence":"first","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, NB, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7698-5336","authenticated-orcid":false,"given":"Haojun","family":"Li","sequence":"additional","affiliation":[{"name":"The Ohio State University, Columbus, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8289-3462","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"The Ohio State University, Columbus, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0045-1648","authenticated-orcid":false,"given":"Odysseas","family":"Papapetrou","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, NB, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7592-748X","authenticated-orcid":false,"given":"John","family":"Paparrizos","sequence":"additional","affiliation":[{"name":"The Ohio State University, Columbus, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"2025. A Structured Study of Multivariate Time-Series Distance Measures. https:\/\/github.com\/TheDatumOrg\/MTSDistEval. Accessed: 2025-03--24."},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/645415.652239"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/we.2057"},{"key":"e_1_2_2_4_1","volume-title":"Aaron Bostrom, James Large, and Eamonn Keogh.","author":"Bagnall Anthony","year":"2017","unstructured":"Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large, and Eamonn Keogh. 2017. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery 31, 3 (01 May 2017), 606--660."},{"key":"e_1_2_2_5_1","volume-title":"Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn J. Keogh.","author":"Bagnall Anthony J.","year":"2018","unstructured":"Anthony J. Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn J. Keogh. 2018. The UEA multivariate time series classification archive, 2018. CoRR abs\/1811.00075 (2018)."},{"key":"e_1_2_2_6_1","volume-title":"Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Bagnall A. J.","unstructured":"A. J. Bagnall and G. J. Janacek. 2004. Clustering time series from ARMA models with clipped data. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Seattle, WA, USA) (KDD '04). Association for Computing Machinery, New York, NY, USA, 49--58."},{"key":"e_1_2_2_7_1","volume-title":"k-shapestream: Probabilistic streaming clustering for electric grid events. In 2021 IEEE Madrid PowerTech","author":"Bariya Mohini","year":"2021","unstructured":"Mohini Bariya, Alexandra von Meier, John Paparrizos, and Michael J Franklin. 2021. k-shapestream: Probabilistic streaming clustering for electric grid events. In 2021 IEEE Madrid PowerTech. IEEE, 1--6 (2021)."},{"key":"e_1_2_2_8_1","volume-title":"Statistical inference for probabilistic functions of finite state Markov chains. The annals of mathematical statistics 37, 6","author":"Baum Leonard E","year":"1966","unstructured":"Leonard E Baum and Ted Petrie. 1966. Statistical inference for probabilistic functions of finite state Markov chains. The annals of mathematical statistics 37, 6 (1966), 1554--1563."},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735471.2735476"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3611540.3611604"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-025-00907-x"},{"key":"e_1_2_2_12_1","volume-title":"Dive into Time-Series Anomaly Detection: A Decade Review. arXiv preprint arXiv:2412.20512","author":"Boniol Paul","year":"2024","unstructured":"Paul Boniol, Qinghua Liu, Mingyi Huang, Themis Palpanas, and John Paparrizos. 2024. Dive into Time-Series Anomaly Detection: A Decade Review. arXiv preprint arXiv:2412.20512 (2024)."},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/3554821.3554879"},{"key":"e_1_2_2_14_1","unstructured":"Paul Boniol John Paparrizos and Themis Palpanas. 2023. New Trends in Time Series Anomaly Detection.. In EDBT. 847--850."},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00409"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476365"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.14778\/3467861.3467863"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00423"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335388"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007568.1007636"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.124"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/568518.568520"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012088469-8.50070-X"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/1325851.1325903"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956808"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.03.067"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/303976.304000"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081966"},{"key":"e_1_2_2_29_1","volume-title":"Support-vector networks. Machine Learning 20, 3 (01","author":"Cortes Corinna","year":"1995","unstructured":"Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine Learning 20, 3 (01 Sep 1995), 273--297."},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389"},{"key":"e_1_2_2_31_1","volume-title":"Proceedings of the 28th international conference on machine learning (ICML-11)","author":"Cuturi Marco","year":"2011","unstructured":"Marco Cuturi. 2011. Fast global alignment kernels. In Proceedings of the 28th international conference on machine learning (ICML-11). 929--936."},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735461.2735463"},{"key":"e_1_2_2_33_1","volume-title":"Statistical Comparisons of Classifiers over Multiple Data Sets. J. Mach. Learn. Res. 7 (dec","author":"Dem\u0161ar Janez","year":"2006","unstructured":"Janez Dem\u0161ar. 2006. Statistical Comparisons of Classifiers over Multiple Data Sets. J. Mach. Learn. Res. 7 (dec 2006), 1--30."},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/1454159.1454226"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735479.2735481"},{"key":"e_1_2_2_36_1","volume-title":"Beyond the Dimensions: A Structured Evaluation of Multivariate Time Series Distance Measures. In 2024 IEEE 40th International Conference on Data EngineeringWorkshops (ICDEW). IEEE, 107--112","author":"Hondt Jens E","year":"2024","unstructured":"Jens E d'Hondt, Odysseas Papapetrou, and John Paparrizos. 2024. Beyond the Dimensions: A Structured Evaluation of Multivariate Time Series Distance Measures. In 2024 IEEE 40th International Conference on Data EngineeringWorkshops (ICDEW). IEEE, 107--112."},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.14778\/3282495.3282498"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.14778\/3368289.3368303"},{"key":"e_1_2_2_39_1","volume-title":"Article 12 (dec","author":"Esling Philippe","year":"2012","unstructured":"Philippe Esling and Carlos Agon. 2012. Time-series data mining. ACM Comput. Surv. 45, 1, Article 12 (dec 2012), 34 pages."},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/191843.191925"},{"key":"e_1_2_2_41_1","volume-title":"Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32","author":"Franceschi Jean-Yves","year":"2019","unstructured":"Jean-Yves Franceschi, Aymeric Dieuleveut, and Martin Jaggi. 2019. Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1937.10503522"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph110302741"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47880-7_3"},{"key":"e_1_2_2_45_1","volume-title":"Kanellakis","author":"Goldin Dina Q.","year":"1995","unstructured":"Dina Q. Goldin and Paris C. Kanellakis. 1995. On similarity queries for time-series data: Constraint specification and implementation. In Principles and Practice of Constraint Programming - CP '95, Ugo Montanari and Francesca Rossi (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 137--153."},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3722212.3725135"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3725420"},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF53345.2021.9723220"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-013-0322-1"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2014.2326100"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/SUTC.2010.29"},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.14778\/3380750.3380761"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457283"},{"key":"e_1_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2001.989529"},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020607"},{"key":"e_1_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316801"},{"key":"e_1_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.5555\/1182635.1164262"},{"key":"e_1_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2017.08.002"},{"key":"e_1_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/253260.253332"},{"key":"e_1_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3352020.3352022"},{"key":"e_1_2_2_61_1","unstructured":"Sergei Lebedev. 2010. hmmlearn. https:\/\/github.com\/hmmlearn\/hmmlearn."},{"key":"e_1_2_2_62_1","volume-title":"Similarity Match Over High Speed Time-Series Streams. In 2007 IEEE 23rd International Conference on Data Engineering. 1086--1095","author":"Lian Xiang","year":"2007","unstructured":"Xiang Lian, Lei Chen, Jeffrey Xu Yu, Guoren Wang, and Ge Yu. 2007. Similarity Match Over High Speed Time-Series Streams. In 2007 IEEE 23rd International Conference on Data Engineering. 1086--1095."},{"key":"e_1_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.14778\/3275366.3284968"},{"key":"e_1_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476305"},{"key":"e_1_2_2_65_1","volume-title":"AdaEdge: A Dynamic Compression Selection Framework for Resource Constrained Devices. In 2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 1506--1519","author":"Liu Chunwei","year":"2024","unstructured":"Chunwei Liu, John Paparrizos, and Aaron J Elmore. 2024. AdaEdge: A Dynamic Compression Selection Framework for Resource Constrained Devices. In 2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 1506--1519."},{"key":"e_1_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.14778\/3685800.3685842"},{"key":"e_1_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3437"},{"key":"e_1_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3631429"},{"key":"e_1_2_2_69_1","volume-title":"Jason Lines, and Franz J. Kir\u00e1ly","author":"L\u00f6ning Markus","year":"2019","unstructured":"Markus L\u00f6ning, Anthony J. Bagnall, Sajaysurya Ganesh, Viktor Kazakov, Jason Lines, and Franz J. Kir\u00e1ly. 2019. sktime: A Unified Interface for Machine Learning with Time Series. CoRR abs\/1909.07872 (2019). arXiv:1909.07872 http:\/\/arxiv.org\/abs\/1909.07872"},{"key":"e_1_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00647-x"},{"key":"e_1_2_2_71_1","volume-title":"Constructing Positive Elastic Kernels with Application to Time Series Classification. CoRR abs\/1005.5141","author":"Marteau Pierre-Fran\u00e7ois","year":"2010","unstructured":"Pierre-Fran\u00e7ois Marteau and Sylvie Gibet. 2010. Constructing Positive Elastic Kernels with Application to Time Series Classification. CoRR abs\/1005.5141 (2010). arXiv:1005.5141 http:\/\/arxiv.org\/abs\/1005.5141"},{"key":"e_1_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.76"},{"key":"e_1_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1002\/asi.23612"},{"key":"e_1_2_2_74_1","volume-title":"MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series","author":"Meng Qianwen","year":"2023","unstructured":"Qianwen Meng, Hangwei Qian, Yong Liu, Lizhen Cui, Yonghui Xu, and Zhiqi Shen. 2023. MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series. In AAAI. AAAI Press, 9153--9161."},{"key":"e_1_2_2_75_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972795.41"},{"key":"e_1_2_2_76_1","unstructured":"Peter Bj\u00f6rn Nemenyi. 1963. Distribution-free Multiple Comparisons. Ph.D. Dissertation. Princeton University."},{"key":"e_1_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/2000824.2000827"},{"key":"e_1_2_2_78_1","unstructured":"Ioannis Paparrizos. 2018. Fast scalable and accurate algorithms for time-series analysis. Ph.D. Dissertation. Columbia University USA."},{"key":"e_1_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2011.5767943"},{"key":"e_1_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551830"},{"key":"e_1_2_2_81_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00268"},{"key":"e_1_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342648"},{"key":"e_1_2_2_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2737793"},{"key":"e_1_2_2_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3044711"},{"key":"e_1_2_2_85_1","doi-asserted-by":"publisher","DOI":"10.14778\/3529337.3529354"},{"key":"e_1_2_2_86_1","volume-title":"A survey on time-series distance measures. arXiv preprint arXiv:2412.20574","author":"Paparrizos John","year":"2024","unstructured":"John Paparrizos, Haojun Li, Fan Yang, Kaize Wu, Jens E d'Hondt, and Odysseas Papapetrou. 2024. A survey on time-series distance measures. arXiv preprint arXiv:2412.20574 (2024)."},{"key":"e_1_2_2_87_1","unstructured":"John Paparrizos Chunwei Liu Bruno Barbarioli Johnny Hwang Ikraduya Edian Aaron J Elmore Michael J Franklin and Sanjay Krishnan. 2021. VergeDB: A Database for IoT Analytics on Edge Devices.. In CIDR."},{"key":"e_1_2_2_88_1","volume-title":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD '20)","author":"Paparrizos John","year":"1887","unstructured":"John Paparrizos, Chunwei Liu, Aaron J. Elmore, and Michael J. Franklin. 2020. Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD '20). Association for Computing Machinery, New York, NY, USA, 1887--1905."},{"key":"e_1_2_2_89_1","volume-title":"Querying Time-Series Data: A Comprehensive Comparison of Distance Measures. Data Engineering","author":"Paparrizos John","year":"2023","unstructured":"John Paparrizos, Chunwei Liu, Aaron J Elmore, and Michael J Franklin. 2023. Querying Time-Series Data: A Comprehensive Comparison of Distance Measures. Data Engineering (2023), 69."},{"key":"e_1_2_2_90_1","doi-asserted-by":"publisher","DOI":"10.14778\/3611540.3611622"},{"key":"e_1_2_2_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939722"},{"key":"e_1_2_2_92_1","doi-asserted-by":"publisher","DOI":"10.1200\/JOP.2015.010504"},{"key":"e_1_2_2_93_1","doi-asserted-by":"publisher","DOI":"10.14778\/3594512.3594530"},{"key":"e_1_2_2_94_1","volume-title":"Bridging the gap: A decade review of time-series clustering methods. arXiv preprint arXiv:2412.20582","author":"Paparrizos John","year":"2024","unstructured":"John Paparrizos, Fan Yang, and Haojun Li. 2024. Bridging the gap: A decade review of time-series clustering methods. arXiv preprint arXiv:2412.20582 (2024)."},{"key":"e_1_2_2_95_1","unstructured":"Pavlos Paraskevopoulos Thanh-Cong Dinh Zolzaya Dashdorj Themis Palpanas Luciano Serafini et al. 2013. Identification and characterization of human behavior patterns from mobile phone data. D4D Challenge session NetMob (2013)."},{"key":"e_1_2_2_96_1","doi-asserted-by":"publisher","DOI":"10.1145\/253260.253264"},{"key":"e_1_2_2_97_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335437"},{"key":"e_1_2_2_98_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1971.10482356"},{"key":"e_1_2_2_99_1","volume-title":"Roddick and Kathleen Stewart Hornsby","author":"John","year":"2001","unstructured":"John F. Roddick and Kathleen Stewart Hornsby. 2001. Temporal, Spatial, and Spatio-Temporal Data Mining. In Lecture Notes in Computer Science."},{"key":"e_1_2_2_100_1","volume-title":"Matthew Middlehurst, and Anthony Bagnall.","author":"Ruiz Alejandro Pasos","year":"2021","unstructured":"Alejandro Pasos Ruiz, Michael Flynn, James Large, Matthew Middlehurst, and Anthony Bagnall. 2021. The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery 35, 2 (01 Mar 2021), 401--449."},{"key":"e_1_2_2_101_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1978.1163055"},{"key":"e_1_2_2_102_1","first-page":"250","article-title":"An Empirical Comparison of Distance Measures for Multivariate Time Series Clustering","volume":"31","author":"Salarpour A","year":"2018","unstructured":"A Salarpour and H Khotanlou. 2018. An Empirical Comparison of Distance Measures for Multivariate Time Series Clustering. International Journal of Engineering 31, 2 (2018), 250--262.","journal-title":"International Journal of Engineering"},{"key":"e_1_2_2_103_1","volume-title":"Tuning time series queries in finance: Case studies and recommendations","author":"Shasha Dennis","year":"1999","unstructured":"Dennis Shasha and Surajit Chaudhuri. 1999. Tuning time series queries in finance: Case studies and recommendations. IEEE Data Engineering Bulletin Special Issue on Performance Tuning for Database Systems (July 1999)."},{"key":"e_1_2_2_104_1","volume-title":"Webb","author":"Shifaz Ahmed","year":"2023","unstructured":"Ahmed Shifaz, Charlotte Pelletier, Fran\u00e7ois Petitjean, and Geoffrey I. Webb. 2023. Elastic similarity and distance measures for multivariate time series. Knowledge and Information Systems 65, 6 (01 Jun 2023), 2665--2698."},{"key":"e_1_2_2_105_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-016-0455-0"},{"key":"e_1_2_2_106_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.88"},{"key":"e_1_2_2_107_1","doi-asserted-by":"publisher","DOI":"10.14778\/3611479.3611536"},{"key":"e_1_2_2_108_1","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514067"},{"key":"e_1_2_2_109_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"e_1_2_2_110_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956777"},{"key":"e_1_2_2_111_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8080876"},{"key":"e_1_2_2_112_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-012-0250--5"},{"key":"e_1_2_2_113_1","doi-asserted-by":"publisher","DOI":"10.2307\/3001968"},{"key":"e_1_2_2_114_1","doi-asserted-by":"publisher","DOI":"10.1145\/3725357"},{"key":"e_1_2_2_115_1","doi-asserted-by":"publisher","DOI":"10.1145\/1032604.1032616"},{"key":"e_1_2_2_116_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0179"},{"key":"e_1_2_2_117_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-017-0519--9"},{"key":"e_1_2_2_118_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20881"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3725258","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T18:58:01Z","timestamp":1774983481000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3725258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,17]]},"references-count":118,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6,17]]}},"alternative-id":["10.1145\/3725258"],"URL":"https:\/\/doi.org\/10.1145\/3725258","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,17]]}}}