{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T04:38:29Z","timestamp":1782016709200,"version":"3.54.5"},"reference-count":302,"publisher":"Association for Computing Machinery (ACM)","issue":"9","license":[{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Government Research Training Program"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a priori. This article surveys the current state of the art in the fast-moving field of deep learning for time series classification and extrinsic regression. We review different network architectures and training methods used for these tasks and discuss the challenges and opportunities when applying deep learning to time series data. We also summarize two critical applications of time series classification and extrinsic regression, human activity recognition and satellite earth observation.<\/jats:p>","DOI":"10.1145\/3649448","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T12:57:33Z","timestamp":1709038653000},"page":"1-45","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":200,"title":["Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2475-6040","authenticated-orcid":false,"given":"Navid","family":"Mohammadi Foumani","sequence":"first","affiliation":[{"name":"Monash University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9899-9059","authenticated-orcid":false,"given":"Lynn","family":"Miller","sequence":"additional","affiliation":[{"name":"Monash University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8377-3241","authenticated-orcid":false,"given":"Chang Wei","family":"Tan","sequence":"additional","affiliation":[{"name":"Monash University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9963-5169","authenticated-orcid":false,"given":"Geoffrey I.","family":"Webb","sequence":"additional","affiliation":[{"name":"Monash University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4960-7554","authenticated-orcid":false,"given":"Germain","family":"Forestier","sequence":"additional","affiliation":[{"name":"Monash University, Melbourne, Australia and IRIMAS, University ofHaute-Alsace, Mulhouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2991-1612","authenticated-orcid":false,"given":"Mahsa","family":"Salehi","sequence":"additional","affiliation":[{"name":"Monash University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0219622006002258"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2379776.2379788"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.03.056"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.02.010"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447744"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.23730"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","unstructured":"A. Rajkomar E. Oren K. Chen A. M. Dai N. Hajaj M. Hardt P. J. Liu X. Liu J. Marcus M. Sun P. Sundberg H. Yee K. Zhang Y. Zhang G. Flores G. E. Duggan J. Irvine Q. Le K. Litsch A. Mossin J. Tansuwan D. Wang J. Wexler J. Wilson D. Ludwig S. L. Volchenboum K. Chou M. Pearson S. Madabushi N. H. Shah A. J. Butte M. D. Howell C. Cui G. S. Corrado J. Dean. 2018. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018 May 8 1 (2018) 18. DOI:10.1038\/s41746-018-0029-1. PMID: 31304302; PMCID: PMC6550175.","DOI":"10.1038\/s41746-018-0029-1"},{"key":"e_1_3_2_9_2","article-title":"The UEA multivariate time series classification archive, 2018","author":"Bagnall Anthony","year":"2018","unstructured":"Anthony Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh. 2018. The UEA multivariate time series classification archive, 2018. arXiv preprint:1811.00075 (2018).","journal-title":"arXiv preprint:1811.00075"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911747"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-021-00745-9"},{"key":"e_1_3_2_12_2","article-title":"Bake off redux: A review and experimental evaluation of recent time series classification algorithms","author":"Middlehurst Matthew","year":"2023","unstructured":"Matthew Middlehurst, Patrick Sch\u00e4fer, and Anthony Bagnall. 2023. Bake off redux: A review and experimental evaluation of recent time series classification algorithms. arXiv preprint arXiv:2304.13029 (2023).","journal-title":"arXiv preprint arXiv:2304.13029"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00710-y"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Navid Mohammadi Foumani Chang Wei Tan Geoffrey I. Webb and Mahsa Salehi. 2024. Improving position encoding of transformers for multivariate time series classification. Data Mining and Knowledge Discovery 38 1 (2024) 22\u201348.","DOI":"10.1007\/s10618-023-00948-2"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00701-z"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00619-1"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"e_1_3_2_18_2","article-title":"Transformers in time series: A survey","author":"Wen Qingsong","year":"2022","unstructured":"Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun. 2022. Transformers in time series: A survey. arXiv preprint:2202.07125 (2022).","journal-title":"arXiv preprint:2202.07125"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/277"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467401"},{"issue":"1","key":"e_1_3_2_21_2","first-page":"857","article-title":"Self-supervised learning: Generative or contrastive","volume":"35","author":"Liu Xiao","year":"2021","unstructured":"Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. 2021. Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2021), 857\u2013876.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/324"},{"key":"e_1_3_2_23_2","first-page":"11808","volume-title":"International Conference on Machine Learning.","author":"Yang Chao-Han Huck","year":"2021","unstructured":"Chao-Han Huck Yang, Yun-Yun Tsai, and Pin-Yu Chen. 2021. Voice2series: Reprogramming acoustic models for time series classification. In International Conference on Machine Learning. PMLR, 11808\u201311819."},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Zhihan Yue Yujing Wang Juanyong Duan Tianmeng Yang Congrui Huang Yunhai Tong and Bixiong Xu. 2022. Ts2vec: Towards universal representation of time series. Proceedings of the AAAI Conference on Artificial Intelligence 36 8 (2022) 8980\u20138987.","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"e_1_3_2_25_2","article-title":"Series2Vec: Similarity-based self-supervised representation learning for time series classification","author":"Foumani Navid Mohammadi","year":"2023","unstructured":"Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I Webb, and Mahsa Salehi. 2023. Series2Vec: Similarity-based self-supervised representation learning for time series classification. arXiv preprint arXiv:2312.03998 (2023).","journal-title":"arXiv preprint arXiv:2312.03998"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-016-0483-9"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","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 (2021) 401\u2013449.","DOI":"10.1007\/s10618-020-00727-3"},{"key":"e_1_3_2_28_2","article-title":"Monash university, UEA, UCR time series regression archive","author":"Tan Chang Wei","year":"2020","unstructured":"Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, and Geoffrey I Webb. 2020. Monash university, UEA, UCR time series regression archive. arXiv preprint:2006.10996 (2020).","journal-title":"arXiv preprint:2006.10996"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2014.01.008"},{"key":"e_1_3_2_30_2","article-title":"Generalized denoising auto-encoders as generative models","volume":"26","author":"Bengio Yoshua","year":"2013","unstructured":"Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent. 2013. Generalized denoising auto-encoders as generative models. Advances in Neural Information Processing Systems 26 (2013), 899\u2013907.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2015.06.034"},{"key":"e_1_3_2_32_2","first-page":"120","volume-title":"CCIA","author":"Serr\u00e0 Joan","year":"2018","unstructured":"Joan Serr\u00e0, Santiago Pascual, and Alexandros Karatzoglou. 2018. Towards a universal neural network encoder for time series. In CCIA. 120\u2013129."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-019-01337-2"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-017-9765-5"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3559540"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115147"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICFHR.2016.0058"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.104971"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103044"},{"key":"e_1_3_2_40_2","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (2012), 1097\u20131105.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.10.013"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2019.01.009"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-08010-9_33"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.5555\/2832747.2832806"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.21629\/JSEE.2017.01.18"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.08.023"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00120"},{"key":"e_1_3_2_52_2","article-title":"Multivariate time series classification using dilated convolutional neural network","author":"Yazdanbakhsh Omolbanin","year":"2019","unstructured":"Omolbanin Yazdanbakhsh and Scott Dick. 2019. Multivariate time series classification using dilated convolutional neural network. arXiv preprint:1905.01697 (2019).","journal-title":"arXiv preprint:1905.01697"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW53433.2021.00099"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_55_2","volume-title":"Workshops at the 29th AAAI Conference on Artificial Intelligence","author":"Wang Zhiguang","year":"2015","unstructured":"Zhiguang Wang and Tim Oates. 2015. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In Workshops at the 29th AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_56_2","first-page":"242","volume-title":"10th International Conference on Machine Vision (ICMV\u201917)","volume":"10696","author":"Hatami Nima","year":"2018","unstructured":"Nima Hatami, Yann Gavet, and Johan Debayle. 2018. Classification of time-series images using deep convolutional neural networks. In 10th International Conference on Machine Vision (ICMV\u201917), Vol. 10696. SPIE, 242\u2013249."},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8621889"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/CCHI.2019.8901913"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20010168"},{"issue":"9","key":"e_1_3_2_60_2","first-page":"17","article-title":"Recurrence plots of dynamical systems","volume":"4","author":"Kamphorst J-P. Eckmann S. Oliffson","year":"1987","unstructured":"J-P. Eckmann S. Oliffson Kamphorst, D. Ruelle, et\u00a0al. 1987. Recurrence plots of dynamical systems. Europhysics Letters 4, 9 (1987), 17.","journal-title":"Europhysics Letters"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.06.032"},{"key":"e_1_3_2_63_2","article-title":"Multi-scale convolutional neural networks for time series classification","author":"Cui Zhicheng","year":"2016","unstructured":"Zhicheng Cui, Wenlin Chen, and Yixin Chen. 2016. Multi-scale convolutional neural networks for time series classification. arXiv preprint:1603.06995 (2016).","journal-title":"arXiv preprint:1603.06995"},{"key":"e_1_3_2_64_2","volume-title":"ECML\/PKDD Workshop on Advanced Analytics and Learning on Temporal Data","author":"Guennec Arthur Le","year":"2016","unstructured":"Arthur Le Guennec, Simon Malinowski, and Romain Tavenard. 2016. Data augmentation for time series classification using convolutional neural networks. In ECML\/PKDD Workshop on Advanced Analytics and Learning on Temporal Data."},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2018.2864702"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.2352\/ISSN.2470-1173.2019.14.COLOR-090"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533440"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22010157"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.08.057"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA60987.2023.10302569"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3078184"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020496"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00706-8"},{"key":"e_1_3_2_76_2","article-title":"Shallow RNN: Accurate time-series classification on resource constrained devices","volume":"32","author":"Dennis Don","year":"2019","unstructured":"Don Dennis, Durmus Alp Emre Acar, Vikram Mandikal, Vinu Sankar Sadasivan, Venkatesh Saligrama, Harsha Vardhan Simhadri, and Prateek Jain. 2019. Shallow RNN: Accurate time-series classification on resource constrained devices. Advances in Neural Information Processing Systems 32 (2019), 11 pages.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_77_2","volume-title":"20th International Joint Conference on Artificial Intelligence (IJCAI\u201907)","author":"Fern\u00e1ndez Santiago","year":"2007","unstructured":"Santiago Fern\u00e1ndez, Alex Graves, and J\u00fcrgen Schmidhuber. 2007. Sequence labelling in structured domains with hierarchical recurrent neural networks. In 20th International Joint Conference on Artificial Intelligence (IJCAI\u201907)."},{"key":"e_1_3_2_78_2","article-title":"Training and analysing deep recurrent neural networks","volume":"26","author":"Hermans Michiel","year":"2013","unstructured":"Michiel Hermans and Benjamin Schrauwen. 2013. Training and analysing deep recurrent neural networks. Advances in Neural Information Processing Systems 26 (2013), 190\u2013198.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_79_2","first-page":"1310","volume-title":"International Conference on Machine Learning.","author":"Pascanu Razvan","year":"2013","unstructured":"Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013. On the difficulty of training recurrent neural networks. In International Conference on Machine Learning. PMLR, 1310\u20131318."},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_81_2","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint:1412.3555 (2014).","journal-title":"arXiv preprint:1412.3555"},{"key":"e_1_3_2_82_2","article-title":"Sequence to sequence learning with neural networks","volume":"27","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems 27 (2014), 3104\u20133112.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298932"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2016.0078"},{"key":"e_1_3_2_86_2","article-title":"TimeNet: Pre-trained deep recurrent neural network for time series classification","author":"Malhotra Pankaj","year":"2017","unstructured":"Pankaj Malhotra, Vishnu TV, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2017. TimeNet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint:1706.08838 (2017).","journal-title":"arXiv preprint:1706.08838"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.04.014"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6165"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00206"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2779939"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2772336"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIIC48513.2020.9065078"},{"issue":"417","key":"e_1_3_2_93_2","first-page":"1","article-title":"Understanding the exploding gradient problem","volume":"2","author":"Pascanu Razvan","year":"2012","unstructured":"Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2012. Understanding the exploding gradient problem. CoRR, abs\/1211.5063 2, 417 (2012), 1.","journal-title":"CoRR, abs\/1211.5063"},{"key":"e_1_3_2_94_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_96_2","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy Alexey","year":"2020","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et\u00a0al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint:2010.11929 (2020).","journal-title":"arXiv preprint:2010.11929"},{"key":"e_1_3_2_97_2","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume":"32","author":"Li Shiyang","year":"2019","unstructured":"Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2021.653659"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00087"},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441815"},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.01.001"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.1109\/BHI.2018.8333405"},{"key":"e_1_3_2_104_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/476"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.11.060"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3040515"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.04.053"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599549"},{"key":"e_1_3_2_109_2","article-title":"Spatial transformer networks","volume":"28","author":"Jaderberg Max","year":"2015","unstructured":"Max Jaderberg, Karen Simonyan, Andrew Zisserman, et\u00a0al. 2015. Spatial transformer networks. Advances in Neural Information Processing Systems 28 (2015), 2017\u20132025.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_110_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASSP54407.2021.00044"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11635"},{"key":"e_1_3_2_114_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114570"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-28650-9_4"},{"key":"e_1_3_2_116_2","article-title":"Paying attention to astronomical transients: Photometric classification with the time-series transformer","author":"Jr. Tarek Allam","year":"2021","unstructured":"Tarek Allam Jr. and Jason D. McEwen. 2021. Paying attention to astronomical transients: Photometric classification with the time-series transformer. arXiv preprint:2105.06178 (2021).","journal-title":"arXiv preprint:2105.06178"},{"key":"e_1_3_2_117_2","article-title":"Gated transformer networks for multivariate time series classification","author":"Liu Minghao","year":"2021","unstructured":"Minghao Liu, Shengqi Ren, Siyuan Ma, Jiahui Jiao, Yizhou Chen, Zhiguang Wang, and Wei Song. 2021. Gated transformer networks for multivariate time series classification. arXiv preprint:2103.14438 (2021).","journal-title":"arXiv preprint:2103.14438"},{"key":"e_1_3_2_118_2","article-title":"Rethinking attention mechanism in time series classification","author":"Zhao Bowen","year":"2022","unstructured":"Bowen Zhao, Huanlai Xing, Xinhan Wang, Fuhong Song, and Zhiwen Xiao. 2022. Rethinking attention mechanism in time series classification. arXiv preprint:2207.07564 (2022).","journal-title":"arXiv preprint:2207.07564"},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-05933-9_12"},{"issue":"8","key":"e_1_3_2_120_2","first-page":"1","article-title":"A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection","volume":"14","author":"Jin Ming","year":"2023","unstructured":"Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, and Shirui Pan. 2023. A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection. arXiv 14, 8 (July2023), 1\u201327. arxiv:2307.03759http:\/\/arxiv.org\/abs\/2307.03759","journal-title":"arXiv"},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_122_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_123_2","article-title":"LB-SimTSC: An efficient similarity-aware graph neural network for semi-supervised time series classification","author":"Xi Wenjie","year":"2023","unstructured":"Wenjie Xi, Arnav Jain, Li Zhang, and Jessica Lin. 2023. LB-SimTSC: An efficient similarity-aware graph neural network for semi-supervised time series classification. arXiv (Jan.2023). arxiv:2301.04838","journal-title":"arXiv"},{"key":"e_1_3_2_124_2","unstructured":"Huaiyuan Liu Xianzhang Liu Donghua Yang Zhiyu Liang Hongzhi Wang Yong Cui and Jun Gu. 2023. TodyNet: Temporal dynamic graph neural network for multivariate time series classification. ArXiv abs\/2304.05078 (2023). https:\/\/api.semanticscholar.org\/CorpusID:258059979"},{"key":"e_1_3_2_125_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-022-00349-6"},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3094908"},{"key":"e_1_3_2_127_2","first-page":"160","volume-title":"Machine Learning for Healthcare Conference","author":"Covert Ian C.","year":"2019","unstructured":"Ian C. Covert, Balu Krishnan, Imad Najm, Jiening Zhan, Matthew Shore, John Hixson, and Ming Jack Po. 2019. Temporal graph convolutional networks for automatic seizure detection. In Machine Learning for Healthcare Conference. PMLR, 160\u2013180."},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2018.2817622"},{"key":"e_1_3_2_129_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/184"},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06084-6"},{"key":"e_1_3_2_131_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2020.3040669"},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.1109\/SPMB52430.2021.9672293"},{"key":"e_1_3_2_133_2","first-page":"1","article-title":"Self-supervised graph neural networks for improved electroencephalographic seizure analysis","author":"Tang Siyi","year":"2021","unstructured":"Siyi Tang, Jared A. Dunnmon, Khaled Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel L. Rubin, and Christopher Lee-Messer. 2021. Self-supervised graph neural networks for improved electroencephalographic seizure analysis. In 10th International Conference on Learning Representations (ICLR\u201922), 1\u201323. arxiv:2104.08336","journal-title":"10th International Conference on Learning Representations"},{"key":"e_1_3_2_134_2","first-page":"1","article-title":"Graph-guided network for irregularly sampled multivariate time series","author":"Zhang Xiang","year":"2021","unstructured":"Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, and Marinka Zitnik. 2021. Graph-guided network for irregularly sampled multivariate time series. In 10th International Conference on Learning Representations (ICLR\u201922), 1\u201321. arxiv:2110.05357","journal-title":"10th International Conference on Learning Representations"},{"key":"e_1_3_2_135_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3055554"},{"key":"e_1_3_2_136_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102471"},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.07.032"},{"key":"e_1_3_2_138_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977172.23"},{"key":"e_1_3_2_139_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2022.3185407"},{"key":"e_1_3_2_140_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3310064"},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS52108.2023.10281458"},{"key":"e_1_3_2_142_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-004-0154-9"},{"key":"e_1_3_2_143_2","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations (ICLR\u201917) - Conference Track Proceedings. 1\u201314. arxiv:1609.02907","journal-title":"5th International Conference on Learning Representations (ICLR\u201917) - Conference Track Proceedings"},{"key":"e_1_3_2_144_2","first-page":"25038","volume-title":"ICML","author":"Yang Ling","year":"2022","unstructured":"Ling Yang and Shenda Hong. 2022. Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion. In ICML. 25038\u201325054."},{"key":"e_1_3_2_145_2","article-title":"Unsupervised feature extraction by time-contrastive learning and nonlinear ICA","volume":"29","author":"Hyvarinen Aapo","year":"2016","unstructured":"Aapo Hyvarinen and Hiroshi Morioka. 2016. Unsupervised feature extraction by time-contrastive learning and nonlinear ICA. Advances in Neural Information Processing Systems 29 (2016), 3772\u20133780.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_146_2","article-title":"Unsupervised scalable representation learning for multivariate time series","volume":"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. NeurIPS 418, 32 (2019), 12 pages.","journal-title":"NeurIPS"},{"key":"e_1_3_2_147_2","unstructured":"Sana Tonekaboni Danny Eytan and Anna Goldenberg. 2021. Unsupervised representation learning for time series with temporal neighborhood coding. International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=8qDwejCuCN"},{"key":"e_1_3_2_148_2","doi-asserted-by":"crossref","unstructured":"Kristoffer Wickstr\u00f8m Michael Kampffmeyer Karl \u00d8yvind Mikalsen and Robert Jenssen. 2022. Mixing up contrastive learning: Self-supervised representation learning for time series. Pattern Recognition Letters 155 (2022) 54\u201361.","DOI":"10.1016\/j.patrec.2022.02.007"},{"key":"e_1_3_2_149_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108606"},{"key":"e_1_3_2_150_2","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS\u201922)","author":"Zhang Xiang","year":"2022","unstructured":"Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik. 2022. Self-supervised contrastive pre-training for time series via time-frequency consistency. In Proceedings of Neural Information Processing Systems (NeurIPS\u201922)."},{"key":"e_1_3_2_151_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26098"},{"key":"e_1_3_2_152_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539329"},{"key":"e_1_3_2_153_2","article-title":"TimeMAE: Self-supervised representations of time series with decoupled masked autoencoders","author":"Cheng Mingyue","year":"2023","unstructured":"Mingyue Cheng, Qi Liu, Zhiding Liu, Hao Zhang, Rujiao Zhang, and Enhong Chen. 2023. TimeMAE: Self-supervised representations of time series with decoupled masked autoencoders. arXiv preprint arXiv:2303.00320 (2023).","journal-title":"arXiv preprint arXiv:2303.00320"},{"key":"e_1_3_2_154_2","article-title":"Self-supervised time series representation learning via cross reconstruction transformer","author":"Zhang Wenrui","year":"2023","unstructured":"Wenrui Zhang, Ling Yang, Shijia Geng, and Shenda Hong. 2023. Self-supervised time series representation learning via cross reconstruction transformer. IEEE Transactions on Neural Networks and Learning Systems (2023), 1\u201310.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_155_2","article-title":"Finding foundation models for time series classification with a PreText task","author":"Ismail-Fawaz Ali","year":"2023","unstructured":"Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, and Germain Forestier. 2023. Finding foundation models for time series classification with a PreText task. arXiv preprint arXiv:2311.14534 (2023).","journal-title":"arXiv preprint arXiv:2311.14534"},{"key":"e_1_3_2_156_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_157_2","doi-asserted-by":"publisher","DOI":"10.1145\/3136755.3136817"},{"key":"e_1_3_2_158_2","first-page":"651","volume-title":"Proceedings of the International Symposium on Automation and Robotics in Construction (ISARC\u201919)","volume":"36","author":"Rashid Khandakar M.","year":"2019","unstructured":"Khandakar M. Rashid and Joseph Louis. 2019. Window-warping: A time series data augmentation of IMU data for construction equipment activity identification. In Proceedings of the International Symposium on Automation and Robotics in Construction (ISARC\u201919), Vol. 36. IAARC Publications, 651\u2013657."},{"key":"e_1_3_2_159_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412812"},{"key":"e_1_3_2_160_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054130"},{"key":"e_1_3_2_161_2","first-page":"471","volume-title":"Interspeech","author":"Vachhani Bhavik","year":"2018","unstructured":"Bhavik Vachhani, Chitralekha Bhat, and Sunil Kumar Kopparapu. 2018. Data augmentation using healthy speech for dysarthric speech recognition. In Interspeech. 471\u2013475."},{"key":"e_1_3_2_162_2","unstructured":"Jingkun Gao Xiaomin Song Qingsong Wen Pichao Wang Liang Sun and Huan Xu. 2020. RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks. arxiv:cs.LG\/2002.09545 (2020)."},{"key":"e_1_3_2_163_2","unstructured":"Zhicheng Cui Wenlin Chen and Yixin Chen. 2016. Multi-Scale Convolutional Neural Networks for Time Series Classification. arxiv:cs.CV\/1603.06995 (2016)."},{"key":"e_1_3_2_164_2","volume-title":"ECML\/PKDD on Advanced Analytics and Learning on Temporal Data","author":"Guennec Arthur Le","year":"2016","unstructured":"Arthur Le Guennec, Simon Malinowski, and Romain Tavenard. 2016. Data augmentation for time series classification using convolutional neural networks. In ECML\/PKDD on Advanced Analytics and Learning on Temporal Data."},{"key":"e_1_3_2_165_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.106"},{"key":"e_1_3_2_166_2","doi-asserted-by":"crossref","unstructured":"Hassan Ismail Fawaz Germain Forestier Jonathan Weber Lhassane Idoumghar and Pierre-Alain Muller. 2018. Data Augmentation Using Synthetic Data for Time Series Classification with Deep Residual Networks. arxiv:cs.CV\/1808.02455 (2018).","DOI":"10.1109\/BigData.2018.8621990"},{"key":"e_1_3_2_167_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI50040.2020.00163"},{"key":"e_1_3_2_168_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0254841"},{"key":"e_1_3_2_169_2","first-page":"117","volume-title":"International Workshop on Advanced Analytics and Learning on Temporal Data","author":"Pialla Gautier","year":"2022","unstructured":"Gautier Pialla, Maxime Devanne, Jonathan Weber, Lhassane Idoumghar, and Germain Forestier. 2022. Data augmentation for time series classification with deep learning models. In International Workshop on Advanced Analytics and Learning on Temporal Data. Springer, 117\u2013132."},{"key":"e_1_3_2_170_2","article-title":"Data augmentation for time-series classification: An extensive empirical study and comprehensive survey","author":"Gao Zijun","year":"2023","unstructured":"Zijun Gao, Lingbo Li, and Tianhua Xu. 2023. Data augmentation for time-series classification: An extensive empirical study and comprehensive survey. arXiv preprint arXiv:2310.10060 (2023).","journal-title":"arXiv preprint arXiv:2310.10060"},{"key":"e_1_3_2_171_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_172_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8621990"},{"key":"e_1_3_2_173_2","article-title":"Transfer learning for time series classification in dissimilarity spaces","volume":"78","author":"Spiegel Stephan","year":"2016","unstructured":"Stephan Spiegel. 2016. Transfer learning for time series classification in dissimilarity spaces. Proceedings of AALTD 78 (2016).","journal-title":"Proceedings of AALTD"},{"key":"e_1_3_2_174_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20154271"},{"key":"e_1_3_2_175_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-07689-3_18"},{"key":"e_1_3_2_176_2","doi-asserted-by":"publisher","DOI":"10.36001\/ijphm.2022.v13i2.3267"},{"key":"e_1_3_2_177_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852105"},{"key":"e_1_3_2_178_2","doi-asserted-by":"publisher","DOI":"10.1080\/03772063.2020.1749143"},{"key":"e_1_3_2_179_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107187"},{"key":"e_1_3_2_180_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-021-17442-1"},{"key":"e_1_3_2_181_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2022.119347"},{"key":"e_1_3_2_182_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10051680"},{"key":"e_1_3_2_183_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42417-021-00286-x"},{"key":"e_1_3_2_184_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3291371"},{"key":"e_1_3_2_185_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.116601"},{"key":"e_1_3_2_186_2","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.13152"},{"key":"e_1_3_2_187_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10116-x"},{"key":"e_1_3_2_188_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3069927"},{"key":"e_1_3_2_189_2","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370441"},{"key":"e_1_3_2_190_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24646-6_10"},{"key":"e_1_3_2_191_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01594-9"},{"key":"e_1_3_2_192_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19051005"},{"key":"e_1_3_2_193_2","doi-asserted-by":"publisher","DOI":"10.3390\/s16010115"},{"key":"e_1_3_2_194_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2012.13"},{"key":"e_1_3_2_195_2","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370438"},{"key":"e_1_3_2_196_2","doi-asserted-by":"publisher","DOI":"10.1109\/INSS.2010.5573462"},{"key":"e_1_3_2_197_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2017.01.008"},{"key":"e_1_3_2_198_2","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2012.110112.00192"},{"key":"e_1_3_2_199_2","doi-asserted-by":"publisher","DOI":"10.1145\/3472290"},{"key":"e_1_3_2_200_2","first-page":"1533","article-title":"Deep, convolutional, and recurrent models for human activity recognition using wearables","author":"Hammerla Nils Y.","year":"2016","unstructured":"Nils Y. Hammerla, Shane Halloran, and Thomas Ploetz. 2016. Deep, convolutional, and recurrent models for human activity recognition using wearables. In International Joint Conference on Artificial Intelligence (IJCAI\u201916). 1533\u20131540. arxiv:1604.08880","journal-title":"International Joint Conference on Artificial Intelligence (IJCAI\u201916)"},{"key":"e_1_3_2_201_2","doi-asserted-by":"publisher","DOI":"10.4108\/icst.mobicase.2014.257786"},{"key":"e_1_3_2_202_2","doi-asserted-by":"publisher","DOI":"10.1145\/2733373.2806333"},{"key":"e_1_3_2_203_2","doi-asserted-by":"publisher","DOI":"10.5555\/2832747.2832806"},{"key":"e_1_3_2_204_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.04.032"},{"key":"e_1_3_2_205_2","doi-asserted-by":"publisher","DOI":"10.1145\/3090076"},{"key":"e_1_3_2_206_2","doi-asserted-by":"publisher","DOI":"10.1109\/BIGCOMP.2017.7881728"},{"key":"e_1_3_2_207_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17112556"},{"key":"e_1_3_2_208_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.09.027"},{"key":"e_1_3_2_209_2","doi-asserted-by":"publisher","DOI":"10.3390\/informatics5020026"},{"key":"e_1_3_2_210_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.12.024"},{"key":"e_1_3_2_211_2","doi-asserted-by":"publisher","DOI":"10.1145\/3267242.3267286"},{"key":"e_1_3_2_212_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/431"},{"key":"e_1_3_2_213_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2890675"},{"key":"e_1_3_2_214_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2949715"},{"key":"e_1_3_2_215_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02283-3"},{"key":"e_1_3_2_216_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10141685"},{"key":"e_1_3_2_217_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-02005-7"},{"key":"e_1_3_2_218_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21051636"},{"key":"e_1_3_2_219_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10030308"},{"key":"e_1_3_2_220_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21062141"},{"key":"e_1_3_2_221_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3045135"},{"key":"e_1_3_2_222_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3132088"},{"key":"e_1_3_2_223_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3158427"},{"key":"e_1_3_2_224_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"},{"key":"e_1_3_2_225_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2007.07.004"},{"key":"e_1_3_2_226_2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-809254-5.00002-6"},{"key":"e_1_3_2_227_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2017.06.031"},{"key":"e_1_3_2_228_2","doi-asserted-by":"publisher","DOI":"10.1080\/20964471.2017.1398903"},{"key":"e_1_3_2_229_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2017.03.015"},{"key":"e_1_3_2_230_2","first-page":"1","article-title":"Attentive weakly supervised land cover mapping for object-based satellite image time series data with spatial interpretation","author":"Ienco Dino","year":"2020","unstructured":"Dino Ienco, Yawogan Jean Eudes Gbodjo, Roberto Interdonato, and Raffaele Gaetano. 2020. Attentive weakly supervised land cover mapping for object-based satellite image time series data with spatial interpretation. arXiv (2020), 1\u201312. arxiv:2004.14672","journal-title":"arXiv"},{"key":"e_1_3_2_231_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01234"},{"key":"e_1_3_2_232_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2022.3180994"},{"key":"e_1_3_2_233_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-56967-x"},{"key":"e_1_3_2_234_2","doi-asserted-by":"publisher","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-1343-2020"},{"key":"e_1_3_2_235_2","article-title":"Deep neural networks for automatic extraction of features in time series optical satellite images","volume":"43","author":"Teyou G. Kamdem De","year":"2020","unstructured":"G. Kamdem De Teyou, Y. Tarabalka, I. Manighetti, R. Almar, and S. Tripodi. 2020. Deep neural networks for automatic extraction of features in time series optical satellite images. International Archives of Photogrammetry, Remote Sensing, & Spatial Information Sciences 43 (2020), 1529\u20131535.","journal-title":"International Archives of Photogrammetry, Remote Sensing, & Spatial Information Sciences"},{"key":"e_1_3_2_236_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs14010209"},{"key":"e_1_3_2_237_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2794581"},{"key":"e_1_3_2_238_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2022.102799"},{"key":"e_1_3_2_239_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.111797"},{"key":"e_1_3_2_240_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.07.010"},{"key":"e_1_3_2_241_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2022.105467"},{"key":"e_1_3_2_242_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs14174378"},{"key":"e_1_3_2_243_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2022.3175609"},{"key":"e_1_3_2_244_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3019046"},{"key":"e_1_3_2_245_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2939152"},{"key":"e_1_3_2_246_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2019.103152"},{"key":"e_1_3_2_247_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs10010075"},{"key":"e_1_3_2_248_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.111946"},{"key":"e_1_3_2_249_2","article-title":"Multimodal crop type classification fusing multi-spectral satellite time series with farmers crop rotations and local crop distribution","author":"Barriere Valentin","year":"2022","unstructured":"Valentin Barriere and Martin Claverie. 2022. Multimodal crop type classification fusing multi-spectral satellite time series with farmers crop rotations and local crop distribution. arXiv preprint:2208.10838 (2022).","journal-title":"arXiv preprint:2208.10838"},{"key":"e_1_3_2_250_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-65742-0_12"},{"key":"e_1_3_2_251_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs13224668"},{"key":"e_1_3_2_252_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3036602"},{"key":"e_1_3_2_253_2","volume-title":"DC@PKDD\/ECML","author":"Mauro Nicola Di","year":"2017","unstructured":"Nicola Di Mauro, Antonio Vergari, Teresa Maria Altomare Basile, Fabrizio G. Ventola, and Floriana Esposito. 2017. End-to-end learning of deep spatio-temporal representations for satellite image time series classification. In DC@PKDD\/ECML."},{"key":"e_1_3_2_254_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2681128"},{"key":"e_1_3_2_255_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs11050523"},{"key":"e_1_3_2_256_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2021.102477"},{"key":"e_1_3_2_257_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.09.016"},{"key":"e_1_3_2_258_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.01.011"},{"key":"e_1_3_2_259_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi7040129"},{"key":"e_1_3_2_260_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs11171986"},{"key":"e_1_3_2_261_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2728698"},{"key":"e_1_3_2_262_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs12172814"},{"key":"e_1_3_2_263_2","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2019.8898458"},{"key":"e_1_3_2_264_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2021.102651"},{"key":"e_1_3_2_265_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2021.102436"},{"key":"e_1_3_2_266_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2020.06.006"},{"key":"e_1_3_2_267_2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2548504"},{"key":"e_1_3_2_268_2","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2019.8900517"},{"key":"e_1_3_2_269_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852316"},{"key":"e_1_3_2_270_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(05)80023-1"},{"key":"e_1_3_2_271_2","first-page":"148","volume-title":"13th International Conference on Machine Learning.","author":"Freund Yoav","year":"1996","unstructured":"Yoav Freund and Robert E. Schapire. 1996. Experiments with a new boosting algorithm. In 13th International Conference on Machine Learning.148\u2013156."},{"key":"e_1_3_2_272_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2016.03.008"},{"key":"e_1_3_2_273_2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2017.2762307"},{"key":"e_1_3_2_274_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.04.015"},{"key":"e_1_3_2_275_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.111716"},{"key":"e_1_3_2_276_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs12183062"},{"key":"e_1_3_2_277_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs13234822"},{"key":"e_1_3_2_278_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0361-2"},{"key":"e_1_3_2_279_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00663-x"},{"key":"e_1_3_2_280_2","article-title":"Amercing: An intuitive, elegant and effective constraint for dynamic time warping","author":"Herrmann Matthieu","year":"2021","unstructured":"Matthieu Herrmann and Geoffrey I. Webb. 2021. Amercing: An intuitive, elegant and effective constraint for dynamic time warping. arXiv preprint:2111.13314 (2021).","journal-title":"arXiv preprint:2111.13314"},{"key":"e_1_3_2_281_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-65742-0_1"},{"key":"e_1_3_2_282_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-06057-9"},{"key":"e_1_3_2_283_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2416723"},{"key":"e_1_3_2_284_2","doi-asserted-by":"publisher","unstructured":"Jason Lines Sarah Taylor and Anthony Bagnall. 2018. Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles. ACM Trans. Knowl. Discov. Data 12 5 Article 52 (October 2018) 35 pages. 10.1145\/3182382","DOI":"10.1145\/3182382"},{"key":"e_1_3_2_285_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0133"},{"key":"e_1_3_2_286_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-015-0418-x"},{"key":"e_1_3_2_287_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-22729-0_20"},{"key":"e_1_3_2_288_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0377-7"},{"key":"e_1_3_2_289_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-013-0322-1"},{"key":"e_1_3_2_290_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.02.030"},{"key":"e_1_3_2_291_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.72"},{"key":"e_1_3_2_292_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467231"},{"key":"e_1_3_2_293_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00844-1"},{"key":"e_1_3_2_294_2","first-page":"1","article-title":"Hydra: Competing convolutional kernels for fast and accurate time series classification","author":"Dempster Angus","year":"2023","unstructured":"Angus Dempster, Daniel F. Schmidt, and Geoffrey I. Webb. 2023. Hydra: Competing convolutional kernels for fast and accurate time series classification. Data Mining and Knowledge Discovery 37 (2023), 1\u201327.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"e_1_3_2_295_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00617-3"},{"key":"e_1_3_2_296_2","article-title":"Proximity forest 2.0: A new effective and scalable similarity-based classifier for time series","author":"Herrmann Matthieu","year":"2023","unstructured":"Matthieu Herrmann, Chang Wei Tan, Mahsa Salehi, and Geoffrey I. Webb. 2023. Proximity forest 2.0: A new effective and scalable similarity-based classifier for time series. arXiv preprint arXiv:2304.05800 (2023).","journal-title":"arXiv preprint arXiv:2304.05800"},{"key":"e_1_3_2_297_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_298_2","volume-title":"ICML","author":"Nair Vinod","year":"2010","unstructured":"Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted Boltzmann machines. In ICML."},{"key":"e_1_3_2_299_2","article-title":"Hierarchical recurrent neural networks for long-term dependencies","volume":"8","author":"Hihi Salah","year":"1995","unstructured":"Salah Hihi and Yoshua Bengio. 1995. Hierarchical recurrent neural networks for long-term dependencies. Advances in Neural Information Processing Systems 8 (1995), 493\u2013499.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_300_2","article-title":"How to construct deep recurrent neural networks","author":"Pascanu Razvan","year":"2013","unstructured":"Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2013. How to construct deep recurrent neural networks. arXiv preprint:1312.6026 (2013).","journal-title":"arXiv preprint:1312.6026"},{"key":"e_1_3_2_301_2","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint:1409.0473 (2014).","journal-title":"arXiv preprint:1409.0473"},{"key":"e_1_3_2_302_2","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint:1406.1078 (2014).","journal-title":"arXiv preprint:1406.1078"},{"key":"e_1_3_2_303_2","article-title":"Effective approaches to attention-based neural machine translation","author":"Luong Minh-Thang","year":"2015","unstructured":"Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv preprint:1508.04025 (2015).","journal-title":"arXiv preprint:1508.04025"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649448","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3649448","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:21Z","timestamp":1750291401000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,25]]},"references-count":302,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,10,31]]}},"alternative-id":["10.1145\/3649448"],"URL":"https:\/\/doi.org\/10.1145\/3649448","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,25]]},"assertion":[{"value":"2023-01-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-31","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}