{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T22:37:13Z","timestamp":1770331033932,"version":"3.49.0"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:p>Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels. However, existing approaches usually adopt the models originally designed for other domains (e.g., computer vision) to encode the time series data and rely on strong assumptions to design learning objectives, which limits their ability to perform well. To deal with these problems, we propose a novel URL framework for multivariate time series by learning time-series-specific shapelet-based representation through a popular contrasting learning paradigm. To the best of our knowledge, this is the first work that explores the shapelet-based embedding in the unsupervised general-purpose representation learning. A unified shapelet-based encoder and a novel learning objective with multi-grained contrasting and multi-scale alignment are particularly designed to achieve our goal, and a data augmentation library is employed to improve the generalization. We conduct extensive experiments using tens of real-world datasets to assess the representation quality on many downstream tasks, including classification, clustering, and anomaly detection. The results demonstrate the superiority of our method against not only URL competitors, but also techniques specially designed for downstream tasks. Our code has been made publicly available at https:\/\/github.com\/real2fish\/CSL.<\/jats:p>","DOI":"10.14778\/3632093.3632103","type":"journal-article","created":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T11:26:31Z","timestamp":1705749991000},"page":"386-399","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["A Shapelet-Based Framework for Unsupervised Multivariate Time Series Representation Learning"],"prefix":"10.14778","volume":"17","author":[{"given":"Zhiyu","family":"Liang","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"given":"Jianfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Chen","family":"Liang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"given":"Hongzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"given":"Zheng","family":"Liang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}]},{"given":"Lujia","family":"Pan","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"International conference on machine learning. PMLR, 1247--1255","author":"Andrew Galen","year":"2013","unstructured":"Galen Andrew, Raman Arora, Jeff Bilmes, and Karen Livescu. 2013. Deep canonical correlation analysis. In International conference on machine learning. PMLR, 1247--1255."},{"key":"e_1_2_1_2_1","series-title":"Time Series Machine Learning Website","volume-title":"Aaron Bostrom, James Large, and Matthew Middlehurst. [n.d.]","author":"Bagnall Anthony","unstructured":"Anthony Bagnall, Eamonn Keogh, Jason Lines, Aaron Bostrom, James Large, and Matthew Middlehurst. [n.d.]. Time Series Machine Learning Website. www.timeseriesclassification.com."},{"key":"e_1_2_1_3_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). arXiv:1811.00075 http:\/\/arxiv.org\/abs\/1811.00075"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Stefanos Bennett Mihai Cucuringu and Gesine Reinert. 2022. Detection and clustering of lead-lag networks for multivariate time series with an application to financial markets. (2022).","DOI":"10.1007\/s10994-022-06250-4"},{"key":"e_1_2_1_5_1","volume-title":"Transactions on","author":"Bostrom Aaron","unstructured":"Aaron Bostrom and Anthony Bagnall. 2017. Binary shapelet transform for multiclass time series classification. In Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXII. Springer, 24--46."},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1488--1497","author":"Chang Xiaobin","year":"2018","unstructured":"Xiaobin Chang, Tao Xiang, and Timothy M Hospedales. 2018. Scalable and effective deep CCA via soft decorrelation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1488--1497."},{"key":"e_1_2_1_7_1","volume-title":"Backpropagation: theory, architectures, and applications","author":"Chauvin Yves","unstructured":"Yves Chauvin and David E Rumelhart. 2013. Backpropagation: theory, architectures, and applications. Psychology press."},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18","volume":"119","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18 July 2020, Virtual Event (Proceedings of Machine Learning Research), Vol. 119. PMLR, 1597--1607. http:\/\/proceedings.mlr.press\/v119\/chen20j.html"},{"key":"e_1_2_1_9_1","unstructured":"Timothy Derrick and Joshua Thomas. 2004. Time series analysis: the cross-correlation function. (2004)."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/324"},{"key":"e_1_2_1_11_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_1_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623613"},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2977--2986","author":"Han Siho","year":"2022","unstructured":"Siho Han and Simon S Woo. 2022. Learning Sparse Latent Graph Representations for Anomaly Detection in Multivariate Time Series. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2977--2986."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-013-0322-1"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219845"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47426-3_25"},{"key":"e_1_2_1_20_1","volume-title":"tsaug: An open-source package for time series data augmentation. Retrieved","author":"Arundo Analytics Inc. 2019.","year":"2023","unstructured":"Arundo Analytics Inc. 2019. tsaug: An open-source package for time series data augmentation. Retrieved January 1, 2023 from https:\/\/tsaug.readthedocs.io\/en\/stable\/references.html"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6--11 July 2015 (JMLR Workshop and Conference Proceedings), Francis R. Bach and David M. Blei (Eds.)","volume":"37","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6--11 July 2015 (JMLR Workshop and Conference Proceedings), Francis R. Bach and David M. Blei (Eds.), Vol. 37.JMLR.org, 448--456. http:\/\/proceedings.mlr.press\/v37\/ioffe15.html"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0254841"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.04.014"},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/TSM.2019.2917521","article-title":"Fault detection and diagnosis using self-attentive convolutional neural networks for variable-length sensor data in semiconductor manufacturing","volume":"32","author":"Kim Eunji","year":"2019","unstructured":"Eunji Kim, Sungzoon Cho, Byeongeon Lee, and Myoungsu Cho. 2019. Fault detection and diagnosis using self-attentive convolutional neural networks for variable-length sensor data in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing 32, 3 (2019), 302--309.","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"e_1_2_1_25_1","volume-title":"Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI","author":"Li Guozhong","year":"2021","unstructured":"Guozhong Li, Byron Choi, Jianliang Xu, Sourav S. Bhowmick, Kwok-Pan Chun, and Grace Lai-Hung Wong. 2021. ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2--9, 2021. AAAI Press, 8375--8383. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17018"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.03.060"},{"key":"e_1_2_1_27_1","volume-title":"Prototypical Contrastive Learning of Unsupervised Representations. In 9th International Conference on Learning Representations, ICLR 2021","author":"Li Junnan","year":"2021","unstructured":"Junnan Li, Pan Zhou, Caiming Xiong, and Steven C. H. Hoi. 2021. Prototypical Contrastive Learning of Unsupervised Representations. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=KmykpuSrjcq"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467075"},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 14627--14636","author":"Li Zhaowen","year":"2022","unstructured":"Zhaowen Li, Yousong Zhu, Fan Yang, Wei Li, Chaoyang Zhao, Yingying Chen, Zhiyang Chen, Jiahao Xie, Liwei Wu, Rui Zhao, et al. 2022. Univip: A unified framework for self-supervised visual pre-training. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 14627--14636."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.03.063"},{"key":"e_1_2_1_31_1","volume-title":"Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles. ACM transactions on knowledge discovery from data 12, 5","author":"Lines Jason","year":"2018","unstructured":"Jason Lines, Sarah Taylor, and Anthony Bagnall. 2018. Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles. ACM transactions on knowledge discovery from data 12, 5 (2018)."},{"key":"e_1_2_1_32_1","volume-title":"Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting. In The Tenth International Conference on Learning Representations, ICLR 2022","author":"Liu Shizhan","year":"2022","unstructured":"Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X. Liu, and Schahram Dustdar. 2022. Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25--29, 2022. OpenReview.net. https:\/\/openreview.net\/forum?id=0EXmFzUn5I"},{"key":"e_1_2_1_33_1","volume-title":"2019 IEEE International Conference on Data Mining (ICDM). IEEE, 1246--1251","author":"Ma Qianli","year":"2019","unstructured":"Qianli Ma, Wanqing Zhuang, and Garrison Cottrell. 2019. Triple-shapelet networks for time series classification. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 1246--1251."},{"key":"e_1_2_1_34_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Ma Qianli","year":"2020","unstructured":"Qianli Ma, Wanqing Zhuang, Sen Li, Desen Huang, and Garrison Cottrell. 2020. Adversarial dynamic shapelet networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 5069--5076."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.11.030"},{"key":"e_1_2_1_36_1","volume-title":"1st International Conference on Learning Representations, ICLR","author":"Mikolov Tom\u00e1s","year":"2013","unstructured":"Tom\u00e1s Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2--4, 2013, Workshop Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1301.3781"},{"key":"e_1_2_1_37_1","unstructured":"Christoph Molnar. 2022. Interpretable Machine Learning (2 ed.). https:\/\/christophm.github.io\/interpretable-ml-book"},{"key":"e_1_2_1_38_1","volume-title":"Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1154--1162","author":"Mueen Abdullah","year":"2011","unstructured":"Abdullah Mueen, Eamonn Keogh, and Neal Young. 2011. Logical-shapelets: an expressive primitive for time series classification. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1154--1162."},{"key":"e_1_2_1_39_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113337"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_2_1_42_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.22381\/ajmr8220214","article-title":"Internet of things-based smart healthcare systems and wireless biomedical sensing devices in monitoring, detection, and prevention of COVID-19","volume":"8","author":"Riley Ann","year":"2021","unstructured":"Ann Riley and Elvira Nica. 2021. Internet of things-based smart healthcare systems and wireless biomedical sensing devices in monitoring, detection, and prevention of COVID-19. American Journal of Medical Research 8, 2 (2021), 51--64.","journal-title":"American Journal of Medical Research"},{"key":"e_1_2_1_43_1","volume-title":"EEG signal processing","author":"Sanei Saeid","unstructured":"Saeid Sanei and Jonathon A Chambers. 2013. EEG signal processing. John Wiley & Sons."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"e_1_2_1_45_1","volume-title":"The Tenth International Conference on Learning Representations, ICLR 2022","author":"Tang Wensi","year":"2022","unstructured":"Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, and Jing Jiang. 2022. Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25--29, 2022. OpenReview.net. https:\/\/openreview.net\/forum?id=PDYs7Z2XFGv"},{"key":"e_1_2_1_46_1","volume-title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. In 9th International Conference on Learning Representations, ICLR 2021","author":"Tonekaboni Sana","year":"2021","unstructured":"Sana Tonekaboni, Danny Eytan, and Anna Goldenberg. 2021. Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=8qDwejCuCN"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974010.101"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3136755.3136817"},{"key":"e_1_2_1_49_1","volume-title":"WaveNet: A Generative Model for Raw Audio. CoRR abs\/1609.03499","author":"van den Oord A\u00e4ron","year":"2016","unstructured":"A\u00e4ron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew W. Senior, and Koray Kavukcuoglu. 2016. WaveNet: A Generative Model for Raw Audio. CoRR abs\/1609.03499 (2016). arXiv:1609.03499 http:\/\/arxiv.org\/abs\/1609.03499"},{"key":"e_1_2_1_50_1","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research 9, 86 (2008), 2579--2605. http:\/\/jmlr.org\/papers\/v9\/vandermaaten08a.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_51_1","volume-title":"Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 5998--6008. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2212.09840"},{"key":"e_1_2_1_53_1","volume-title":"A Survey on Multi-view Learning. CoRR abs\/1304.5634","author":"Xu Chang","year":"2013","unstructured":"Chang Xu, Dacheng Tao, and Chao Xu. 2013. A Survey on Multi-view Learning. CoRR abs\/1304.5634 (2013). arXiv:1304.5634 http:\/\/arxiv.org\/abs\/1304.5634"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00064"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01522"},{"key":"e_1_2_1_56_1","volume-title":"International Conference on Machine Learning. PMLR, 25038--25054","author":"Yang Ling","year":"2022","unstructured":"Ling Yang and Shenda Hong. 2022. Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion. In International Conference on Machine Learning. PMLR, 25038--25054."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539274"},{"key":"e_1_2_1_58_1","volume-title":"Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data mining and knowledge discovery 22, 1--2","author":"Ye Lexiang","year":"2011","unstructured":"Lexiang Ye and Eamonn Keogh. 2011. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data mining and knowledge discovery 22, 1--2 (2011), 149--182."},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467401"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3198411"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2847699"},{"key":"e_1_2_1_63_1","volume-title":"The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI2020","author":"Zhang Xuchao","year":"2020","unstructured":"Xuchao Zhang, Yifeng Gao, Jessica Lin, and Chang-Tien Lu. 2020. TapNet: Multivariate Time Series Classification with Attentional Prototypical Network. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7--12, 2020. AAAI Press, 6845--6852. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/6165"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2206.08496"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21428"},{"key":"e_1_2_1_66_1","volume-title":"Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research)","author":"Zhou Tian","unstructured":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. In Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (Eds.), Vol. 162. PMLR, 27268--27286."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3632093.3632103","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T11:32:10Z","timestamp":1705750330000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3632093.3632103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11]]},"references-count":66,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["10.14778\/3632093.3632103"],"URL":"https:\/\/doi.org\/10.14778\/3632093.3632103","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2023,11]]},"assertion":[{"value":"2024-01-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}