{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T12:24:46Z","timestamp":1769948686068,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":58,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Foshan HKUST Projects","award":["FSUST20-FYTRI03B"],"award-info":[{"award-number":["FSUST20-FYTRI03B"]}]},{"name":"NSFC Grant","award":["No. 62206067"],"award-info":[{"award-number":["No. 62206067"]}]},{"name":"Guangzhou-HKUST(GZ) Joint Funding Scheme","award":["2023A03J0673"],"award-info":[{"award-number":["2023A03J0673"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3614759","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:42Z","timestamp":1697874342000},"page":"3308-3318","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["A Co-training Approach for Noisy Time Series Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5354-582X","authenticated-orcid":false,"given":"Weiqi","family":"Zhang","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong SAR, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6695-0617","authenticated-orcid":false,"given":"Jianfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6362-4385","authenticated-orcid":false,"given":"Jia","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou) &amp; Hong Kong University of Science and Technology, Guangzhou &amp; Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0575-8254","authenticated-orcid":false,"given":"Fugee","family":"Tsung","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou) &amp; Hong Kong University of Science and Technology, Guangzhou &amp; Hong Kong SAR, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Deep Extreme Mixture Model for Time Series Forecasting. In 31st ACM International Conference on Information and Knowledge Management: CIKM","author":"Abilasha S","year":"2022","unstructured":"S Abilasha , Sahely Bhadra , Ahmed Zaheer Dadarkar , and P Deepak . 2022 . Deep Extreme Mixture Model for Time Series Forecasting. In 31st ACM International Conference on Information and Knowledge Management: CIKM 2022. S Abilasha, Sahely Bhadra, Ahmed Zaheer Dadarkar, and P Deepak. 2022. Deep Extreme Mixture Model for Time Series Forecasting. In 31st ACM International Conference on Information and Knowledge Management: CIKM 2022."},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 21th international European symposium on artificial neural networks, computational intelligence and machine learning. 437--442","author":"Anguita Davide","year":"2013","unstructured":"Davide Anguita , Alessandro Ghio , Luca Oneto , Xavier Parra Perez , and Jorge Luis Reyes Ortiz . 2013 . A public domain dataset for human activity recognition using smartphones . In Proceedings of the 21th international European symposium on artificial neural networks, computational intelligence and machine learning. 437--442 . Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra Perez, and Jorge Luis Reyes Ortiz. 2013. A public domain dataset for human activity recognition using smartphones. In Proceedings of the 21th international European symposium on artificial neural networks, computational intelligence and machine learning. 437--442."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.04.005"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2006.12.004"},{"key":"e_1_3_2_1_6_1","volume-title":"Time series: applications to finance","author":"Chan Ngai Hang","unstructured":"Ngai Hang Chan . 2004. Time series: applications to finance . John Wiley & Sons . Ngai Hang Chan. 2004. Time series: applications to finance. John Wiley & Sons."},{"key":"e_1_3_2_1_7_1","volume-title":"International conference on machine learning. PMLR, 1597--1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International conference on machine learning. PMLR, 1597--1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2017.0387"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539329"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00262"},{"key":"e_1_3_2_1_11_1","first-page":"3","article-title":"STL: A Seasonal-Trend Decomposition Procedure Based on Loess (with Discussion)","volume":"6","author":"Cleveland Robert B.","year":"1990","unstructured":"Robert B. Cleveland , William S. Cleveland , Jean E. McRae , and Irma Terpenning . 1990 . STL: A Seasonal-Trend Decomposition Procedure Based on Loess (with Discussion) . Journal of Official Statistics , Vol. 6 (1990), 3 -- 73 . Robert B. Cleveland, William S. Cleveland, Jean E. McRae, and Irma Terpenning. 1990. STL: A Seasonal-Trend Decomposition Procedure Based on Loess (with Discussion). Journal of Official Statistics, Vol. 6 (1990), 3--73.","journal-title":"Journal of Official Statistics"},{"key":"e_1_3_2_1_12_1","volume-title":"Time series analysis","author":"Cryer Jonathan D","unstructured":"Jonathan D Cryer . 1986. Time series analysis . Vol. 286 . Springer . Jonathan D Cryer. 1986. Time series analysis. Vol. 286. Springer."},{"key":"e_1_3_2_1_13_1","volume-title":"Advances in Neural Information Processing Systems","volume":"14","author":"Dasgupta Sanjoy","year":"2001","unstructured":"Sanjoy Dasgupta , Michael Littman , and David McAllester . 2001 . PAC generalization bounds for co-training . Advances in Neural Information Processing Systems , Vol. 14 (2001). Sanjoy Dasgupta, Michael Littman, and David McAllester. 2001. PAC generalization bounds for co-training. Advances in Neural Information Processing Systems, Vol. 14 (2001)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467330"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482315"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/324"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467137"},{"key":"e_1_3_2_1_18_1","volume-title":"Dick Van Dijk, et al","author":"Franses Philip Hans","year":"2000","unstructured":"Philip Hans Franses , Dick Van Dijk, et al . 2000 . Non-linear time series models in empirical finance. Cambridge university press . Philip Hans Franses, Dick Van Dijk, et al. 2000. Non-linear time series models in empirical finance. Cambridge university press."},{"key":"e_1_3_2_1_19_1","volume-title":"Robust time series denoising with learnable wavelet packet transform. arXiv preprint arXiv:2206.06126","author":"Frusque Gaetan","year":"2022","unstructured":"Gaetan Frusque and Olga Fink . 2022. Robust time series denoising with learnable wavelet packet transform. arXiv preprint arXiv:2206.06126 ( 2022 ). Gaetan Frusque and Olga Fink. 2022. Robust time series denoising with learnable wavelet packet transform. arXiv preprint arXiv:2206.06126 (2022)."},{"key":"e_1_3_2_1_20_1","volume-title":"Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley.","author":"Goldberger Ary L","year":"2000","unstructured":"Ary L Goldberger , Luis AN Amaral , Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. 2000 . PhysioBank, Physio Toolkit , and PhysioNet : components of a new research resource for complex physiologic signals. circulation, Vol. 101 , 23 (2000), e215--e220. Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. 2000. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, Vol. 101, 23 (2000), e215--e220."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467275"},{"key":"e_1_3_2_1_22_1","first-page":"5679","article-title":"Self-supervised co-training for video representation learning","volume":"33","author":"Han Tengda","year":"2020","unstructured":"Tengda Han , Weidi Xie , and Andrew Zisserman . 2020 . Self-supervised co-training for video representation learning . Advances in Neural Information Processing Systems , Vol. 33 (2020), 5679 -- 5690 . Tengda Han, Weidi Xie, and Andrew Zisserman. 2020. Self-supervised co-training for video representation learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 5679--5690.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_23_1","first-page":"5000","article-title":"Provable guarantees for self-supervised deep learning with spectral contrastive loss","volume":"34","author":"HaoChen Jeff Z","year":"2021","unstructured":"Jeff Z HaoChen , Colin Wei , Adrien Gaidon , and Tengyu Ma . 2021 . Provable guarantees for self-supervised deep learning with spectral contrastive loss . Advances in Neural Information Processing Systems , Vol. 34 (2021), 5000 -- 5011 . Jeff Z HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma. 2021. Provable guarantees for self-supervised deep learning with spectral contrastive loss. Advances in Neural Information Processing Systems, Vol. 34 (2021), 5000--5011.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557432"},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of the 28th international conference on machine learning (ICML-11)","author":"Kumar Abhishek","year":"2011","unstructured":"Abhishek Kumar and Hal Daum\u00e9 . 2011 . A co-training approach for multi-view spectral clustering . In Proceedings of the 28th international conference on machine learning (ICML-11) . 393--400. Abhishek Kumar and Hal Daum\u00e9. 2011. A co-training approach for multi-view spectral clustering. In Proceedings of the 28th international conference on machine learning (ICML-11). 393--400."},{"key":"e_1_3_2_1_26_1","volume-title":"Daphne Ngar-yin Mah, and Grace LH Wong","author":"Li Guozhong","year":"2022","unstructured":"Guozhong Li , Byron Choi , Jianliang Xu , Sourav S Bhowmick , Daphne Ngar-yin Mah, and Grace LH Wong . 2022 b. IPS : Instance Profile for Shapelet Discovery for Time Series Classification. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE , 1781--1793. Guozhong Li, Byron Choi, Jianliang Xu, Sourav S Bhowmick, Daphne Ngar-yin Mah, and Grace LH Wong. 2022b. IPS: Instance Profile for Shapelet Discovery for Time Series Classification. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 1781--1793."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330847"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Jia Li Yu Rong Helen Meng Zhihui Lu Timothy Kwok and Hong Cheng. 2018. TATC: predicting Alzheimer's disease with actigraphy data. In SIGKDD. 509--518.  Jia Li Yu Rong Helen Meng Zhihui Lu Timothy Kwok and Hong Cheng. 2018. TATC: predicting Alzheimer's disease with actigraphy data. In SIGKDD. 509--518.","DOI":"10.1145\/3219819.3219831"},{"key":"e_1_3_2_1_29_1","volume-title":"Prototypical Contrastive Learning of Unsupervised Representations. In International Conference on Learning Representations.","author":"Li Junnan","year":"2020","unstructured":"Junnan Li , Pan Zhou , Caiming Xiong , and Steven Hoi . 2020 . Prototypical Contrastive Learning of Unsupervised Representations. In International Conference on Learning Representations. Junnan Li, Pan Zhou, Caiming Xiong, and Steven Hoi. 2020. Prototypical Contrastive Learning of Unsupervised Representations. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00471"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539140"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2009.07.007"},{"key":"e_1_3_2_1_33_1","volume-title":"A new method for detecting atrial fibrillation using RR intervals. Computers in Cardiology","author":"Moody George","year":"1983","unstructured":"George Moody . 1983. A new method for detecting atrial fibrillation using RR intervals. Computers in Cardiology ( 1983 ), 227--230. George Moody. 1983. A new method for detecting atrial fibrillation using RR intervals. Computers in Cardiology (1983), 227--230."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354805"},{"key":"e_1_3_2_1_35_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 ). 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_3_2_1_36_1","volume-title":"Weak supervision as an efficient approach for automated seizure detection in electroencephalography. NPJ digital medicine","author":"Saab Khaled","year":"2020","unstructured":"Khaled Saab , Jared Dunnmon , Christopher R\u00e9 , Daniel Rubin , and Christopher Lee-Messer . 2020. Weak supervision as an efficient approach for automated seizure detection in electroencephalography. NPJ digital medicine , Vol. 3 , 1 ( 2020 ), 1--12. Khaled Saab, Jared Dunnmon, Christopher R\u00e9, Daniel Rubin, and Christopher Lee-Messer. 2020. Weak supervision as an efficient approach for automated seizure detection in electroencephalography. NPJ digital medicine, Vol. 3, 1 (2020), 1--12."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539396"},{"key":"e_1_3_2_1_38_1","volume-title":"Enzo Tagliazucchi, Helmut Laufs, Peter Vuust, Gustavo Deco, Mark W Woolrich, et al.","author":"Stevner ABA","year":"2019","unstructured":"ABA Stevner , Diego Vidaurre , Joana Cabral , K Rapuano , S\u00f8ren F\u00f8ns Vind Nielsen , Enzo Tagliazucchi, Helmut Laufs, Peter Vuust, Gustavo Deco, Mark W Woolrich, et al. 2019 . Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nature communications, Vol. 10, 1 (2019), 1--14. ABA Stevner, Diego Vidaurre, Joana Cabral, K Rapuano, S\u00f8ren F\u00f8ns Vind Nielsen, Enzo Tagliazucchi, Helmut Laufs, Peter Vuust, Gustavo Deco, Mark W Woolrich, et al. 2019. Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nature communications, Vol. 10, 1 (2019), 1--14."},{"key":"e_1_3_2_1_39_1","volume-title":"Exploring contrastive learning in human activity recognition for healthcare. arXiv preprint arXiv:2011.11542","author":"Tang Chi Ian","year":"2020","unstructured":"Chi Ian Tang , Ignacio Perez-Pozuelo , Dimitris Spathis , and Cecilia Mascolo . 2020. Exploring contrastive learning in human activity recognition for healthcare. arXiv preprint arXiv:2011.11542 ( 2020 ). Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, and Cecilia Mascolo. 2020. Exploring contrastive learning in human activity recognition for healthcare. arXiv preprint arXiv:2011.11542 (2020)."},{"key":"e_1_3_2_1_40_1","volume-title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. In International Conference on Learning Representations.","author":"Tonekaboni Sana","year":"2020","unstructured":"Sana Tonekaboni , Danny Eytan , and Anna Goldenberg . 2020 . Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. In International Conference on Learning Representations. Sana Tonekaboni, Danny Eytan, and Anna Goldenberg. 2020. Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_41_1","volume-title":"PiCO: Contrastive Label Disambiguation for Partial Label Learning. In International Conference on Learning Representations.","author":"Wang Haobo","year":"2021","unstructured":"Haobo Wang , Ruixuan Xiao , Yixuan Li , Lei Feng , Gang Niu , Gang Chen , and Junbo Zhao . 2021 . PiCO: Contrastive Label Disambiguation for Partial Label Learning. In International Conference on Learning Representations. Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, and Junbo Zhao. 2021. PiCO: Contrastive Label Disambiguation for Partial Label Learning. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_42_1","volume-title":"International Conference on Machine Learning. PMLR, 9929--9939","author":"Wang Tongzhou","year":"2020","unstructured":"Tongzhou Wang and Phillip Isola . 2020 . Understanding contrastive representation learning through alignment and uniformity on the hypersphere . In International Conference on Machine Learning. PMLR, 9929--9939 . Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International Conference on Machine Learning. PMLR, 9929--9939."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74958-5_42"},{"key":"e_1_3_2_1_44_1","volume-title":"Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478","author":"Wen Qingsong","year":"2020","unstructured":"Qingsong Wen , Liang Sun , Fan Yang , Xiaomin Song , Jingkun Gao , Xue Wang , and Huan Xu. 2020. Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478 ( 2020 ). Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, and Huan Xu. 2020. Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478 (2020)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2022.02.007"},{"key":"e_1_3_2_1_46_1","unstructured":"Gerald Woo Chenghao Liu Doyen Sahoo Akshat Kumar and Steven Hoi. [n. d.]. CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting. In International Conference on Learning Representations.  Gerald Woo Chenghao Liu Doyen Sahoo Akshat Kumar and Steven Hoi. [n. d.]. CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_47_1","volume-title":"The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ju_Uqw384Oq","author":"Wu Haixu","year":"2023","unstructured":"Haixu Wu , Tengge Hu , Yong Liu , Hang Zhou , Jianmin Wang , and Mingsheng Long . 2023 . TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis . In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ju_Uqw384Oq Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2023. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ju_Uqw384Oq"},{"key":"e_1_3_2_1_48_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 . 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_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539274"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557122"},{"key":"e_1_3_2_1_51_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 1336--1358","author":"Yoon TaeHo","year":"2022","unstructured":"TaeHo Yoon , Youngsuk Park , Ernest K Ryu , and Yuyang Wang . 2022 . Robust probabilistic time series forecasting . In International Conference on Artificial Intelligence and Statistics. PMLR, 1336--1358 . TaeHo Yoon, Youngsuk Park, Ernest K Ryu, and Yuyang Wang. 2022. Robust probabilistic time series forecasting. In International Conference on Artificial Intelligence and Statistics. PMLR, 1336--1358."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467401"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557470"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599543"},{"key":"e_1_3_2_1_56_1","unstructured":"Xiang Zhang Ziyuan Zhao Theodoros Tsiligkaridis and Marinka Zitnik. 2022a. Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency. In Advances in Neural Information Processing Systems Alice H. Oh Alekh Agarwal Danielle Belgrave and Kyunghyun Cho (Eds.). https:\/\/openreview.net\/forum?id=OJ4mMfGKLN  Xiang Zhang Ziyuan Zhao Theodoros Tsiligkaridis and Marinka Zitnik. 2022a. Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency. In Advances in Neural Information Processing Systems Alice H. Oh Alekh Agarwal Danielle Belgrave and Kyunghyun Cho (Eds.). https:\/\/openreview.net\/forum?id=OJ4mMfGKLN"},{"key":"e_1_3_2_1_57_1","volume-title":"2023 b. Robust Recurrent Neural Networks for Time Series Forecasting. Neurocomputing","author":"Zhang Xueli","year":"2023","unstructured":"Xueli Zhang , Cankun Zhong , Jianjun Zhang , Ting Wang , and Wing WY Ng . 2023 b. Robust Recurrent Neural Networks for Time Series Forecasting. Neurocomputing ( 2023 ). Xueli Zhang, Cankun Zhong, Jianjun Zhang, Ting Wang, and Wing WY Ng. 2023 b. Robust Recurrent Neural Networks for Time Series Forecasting. Neurocomputing (2023)."},{"key":"e_1_3_2_1_58_1","volume-title":"International Conference on Machine Learning. PMLR, 27268--27286","author":"Zhou Tian","year":"2022","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 International Conference on Machine Learning. PMLR, 27268--27286 . 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 International Conference on Machine Learning. PMLR, 27268--27286."}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614759","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3614759","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:31Z","timestamp":1750178791000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":58,"alternative-id":["10.1145\/3583780.3614759","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3614759","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}