{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:16:31Z","timestamp":1740132991708,"version":"3.37.3"},"reference-count":70,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFC1523200"],"award-info":[{"award-number":["2020YFC1523200"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106021","U20A20225"],"award-info":[{"award-number":["62106021","U20A20225"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1109\/tkde.2023.3335240","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T19:59:04Z","timestamp":1700596744000},"page":"5106-5119","source":"Crossref","is-referenced-by-count":0,"title":["Discovering Predictable Latent Factors for Time Series Forecasting"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0172-3115","authenticated-orcid":false,"given":"Jingyi","family":"Hou","sequence":"first","affiliation":[{"name":"School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4494-129X","authenticated-orcid":false,"given":"Zhen","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Yan&#x0027;an University, Yan&#x0027;an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4336-6777","authenticated-orcid":false,"given":"Jiayu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9522-4178","authenticated-orcid":false,"given":"Zhijie","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00630"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d16-1053"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1183"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2854193"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108218"},{"author":"Wu","key":"ref7","article-title":"HIST: A graph-based framework for stock trend forecasting via mining concept-oriented shared information"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20369"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105963"},{"article-title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","volume-title":"Proc. Int. Conf. Learn. Represent","author":"Oreshkin","key":"ref10"},{"key":"ref11","first-page":"22419","article-title":"AutoFormer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Adv. Neural Inform. Process. Syst","author":"Wu"},{"issue":"1","key":"ref12","first-page":"3","article-title":"STL: A seasonal-trend decomposition","volume":"6","author":"Cleveland","year":"1990","journal-title":"J. Off. Stat."},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1998.0193"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1142\/IMS"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2288675"},{"key":"ref17","first-page":"6989","article-title":"N-HiTS: Neural hierarchical interpolation for time series forecasting","volume-title":"Proc. AAAI Conf. Artif. Intell.","author":"Challu"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2307\/1912017"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1765(86)90168-0"},{"volume-title":"Time Series Analysis: Forecasting and Control","year":"2015","author":"Box","key":"ref20"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.23.7.768"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.01.026"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.2307\/2977928"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.2307\/2329112"},{"key":"ref25","first-page":"7796","article-title":"Deep state space models for time series forecasting","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Rangapuram"},{"issue":"3","key":"ref26","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","article-title":"DeepAR: Probabilistic forecasting with autoregressive recurrent networks","volume":"36","author":"Flunkert","year":"2020","journal-title":"Int. J. Forecast."},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/366"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11635"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/810"},{"article-title":"Conditional time series forecasting with convolutional neural networks","year":"2017","author":"Borovykh","key":"ref30"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"ref32","first-page":"4838","article-title":"Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Sen"},{"article-title":"Reformer: The efficient transformer","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kitaev","key":"ref33"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref35","first-page":"9881","article-title":"Non-stationary transformers: Exploring the stationarity in time series forecasting","volume-title":"Proc. Adv. Neural Inform. Process. Syst","author":"Liu"},{"key":"ref36","first-page":"27268","article-title":"FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhou"},{"key":"ref37","first-page":"2555","article-title":"Learning latent dynamics for planning from pixels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hafner"},{"key":"ref38","first-page":"544","article-title":"Recurrent Kalman networks: Factorized inference in high-dimensional deep feature spaces","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Becker"},{"key":"ref39","first-page":"29246","article-title":"Clockwork variational autoencoders","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Saxena"},{"key":"ref40","first-page":"2207","article-title":"Variational autoencoders and nonlinear ICA: A unifying framework","volume-title":"Proc. Int. Conf. Artif. Intell. Stat.","author":"Khemakhem"},{"key":"ref41","first-page":"1624","article-title":"Disentangling identifiable features from noisy data with structured nonlinear ICA","volume-title":"Proc. Adv. Neural Inform. Process. Syst","author":"H\u00e4lv\u00e4"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269269"},{"article-title":"HATS: A hierarchical graph attention network for stock movement prediction","year":"2019","author":"Kim","key":"ref43"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"article-title":"TAMP-S2GCNets: Coupling time-aware multipersistence knowledge representation with spatio-supra graph convolutional networks for time-series forecasting","volume-title":"Proc. Int. Conf. Learn. Represent","author":"Chen","key":"ref45"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330983"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(92)90072-Y"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1684(91)90079-X"},{"article-title":"MICN: Multi-scale local and global context modeling for long-term series forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang","key":"ref49"},{"article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","year":"2018","author":"Bai","key":"ref50"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"ref52","first-page":"77","article-title":"Dilated recurrent neural networks","volume-title":"Proc. Adv. Neural Inform. Process. Syst","author":"Chang"},{"key":"ref53","article-title":"Fractals and intrinsic time: A challenge to econometricians","author":"M\u00fcller","year":"1993","journal-title":"Unpublished Manuscript"},{"article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","key":"ref54"},{"article-title":"A new perspective on \u201chow graph neural networks go beyond Weisfeiler-Lehman?\u201d","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wijesinghe","key":"ref55"},{"key":"ref56","first-page":"4114","article-title":"Challenging common assumptions in the unsupervised learning of disentangled representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Locatello"},{"key":"ref57","first-page":"3765","article-title":"Unsupervised feature extraction by time-contrastive learning and nonlinear ICA","volume-title":"Proc. Adv. Neural Inform. Process. Syst","author":"Hyv\u00e4rinen"},{"article-title":"A time series is worth 64 words: Long-term forecasting with transformers","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Nie","key":"ref58"},{"volume-title":"Time-Series","year":"1976","author":"Anderson","key":"ref59"},{"article-title":"Qlib: An AI-oriented quantitative investment platform","year":"2020","author":"Yang","key":"ref60"},{"article-title":"beta-VAE: Learning basic visual concepts with a constrained variational framework","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Higgins","key":"ref61"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","year":"2014","author":"Chung","key":"ref63"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/UKSim.2014.67"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098117"},{"article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Velickovic","key":"ref66"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/640"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467358"},{"article-title":"Understanding disentangling in -VAE","year":"2018","author":"Burgess","key":"ref69"},{"key":"ref70","first-page":"2654","article-title":"Disentangling by factorising","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10679113\/10324313.pdf?arnumber=10324313","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T18:49:34Z","timestamp":1726253374000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10324313\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":70,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2023.3335240","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"type":"print","value":"1041-4347"},{"type":"electronic","value":"1558-2191"},{"type":"electronic","value":"2326-3865"}],"subject":[],"published":{"date-parts":[[2024,10]]}}}