{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T22:15:06Z","timestamp":1783376106347,"version":"3.54.6"},"reference-count":63,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing &amp; Management"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.ipm.2026.104999","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:14:15Z","timestamp":1783095255000},"page":"104999","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Disentangled temporal modeling with sparse convolutions for time series forecasting"],"prefix":"10.1016","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0996-2748","authenticated-orcid":false,"given":"Mingxin","family":"Teng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiye","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunze","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ipm.2026.104999_b1","series-title":"Diffusion-based time series imputation and forecasting with structured state space models","author":"Alcaraz","year":"2023"},{"key":"10.1016\/j.ipm.2026.104999_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102589","article-title":"MixMamba: Time series modeling with adaptive expertise","volume":"112","author":"Alkilane","year":"2024","journal-title":"Information Fusion"},{"key":"10.1016\/j.ipm.2026.104999_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102180","article-title":"Multivariate solar power time series forecasting using multilevel data fusion and deep neural networks","volume":"104","author":"Almaghrabi","year":"2024","journal-title":"Information Fusion"},{"key":"10.1016\/j.ipm.2026.104999_b4","series-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"10.1016\/j.ipm.2026.104999_b5","series-title":"MambaTS: Improved selective state space models for long-term time series forecasting","author":"Cai","year":"2024"},{"key":"10.1016\/j.ipm.2026.104999_b6","series-title":"2024 IEEE 40th international conference on data engineering","first-page":"625","article-title":"TimeDRL: Disentangled representation learning for multivariate time-series","author":"Chang","year":"2024"},{"key":"10.1016\/j.ipm.2026.104999_b7","series-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","author":"Chen","year":"2016"},{"key":"10.1016\/j.ipm.2026.104999_b8","series-title":"TSMixer: An all-MLP architecture for time series forecasting","author":"Chen","year":"2023"},{"key":"10.1016\/j.ipm.2026.104999_b9","series-title":"Generating long sequences with sparse transformers","author":"Child","year":"2019"},{"key":"10.1016\/j.ipm.2026.104999_b10","series-title":"Proceedings of the thirty-first international joint conference on artificial intelligence","first-page":"1994","article-title":"Triformer: Triangular, variable-specific attentions for long sequence multivariate time series forecasting","author":"Cirstea","year":"2022"},{"key":"10.1016\/j.ipm.2026.104999_b11","series-title":"Long-term forecasting with TiDE: Time-series dense encoder","author":"Das","year":"2024"},{"key":"10.1016\/j.ipm.2026.104999_b12","series-title":"Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity","author":"Fedus","year":"2022"},{"issue":"1","key":"10.1016\/j.ipm.2026.104999_b13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1049\/cit2.12060","article-title":"Deep learning for time series forecasting: The electric load case","volume":"7","author":"Gasparin","year":"2022","journal-title":"CAAI Transactions on Intelligence Technology"},{"key":"10.1016\/j.ipm.2026.104999_b14","first-page":"19622","article-title":"Large language models are zero-shot time series forecasters","volume":"Vol. 36","author":"Gruver","year":"2023"},{"key":"10.1016\/j.ipm.2026.104999_b15","series-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2024"},{"key":"10.1016\/j.ipm.2026.104999_b16","first-page":"64145","article-title":"SOFTS: Efficient multivariate time series forecasting with series-core fusion","volume":"Vol. 37","author":"Han","year":"2024"},{"key":"10.1016\/j.ipm.2026.104999_b17","series-title":"Toward controlled generation of text","author":"Hu","year":"2018"},{"key":"10.1016\/j.ipm.2026.104999_b18","series-title":"International conference on learning representations","article-title":"Time-LLM: Time series forecasting by reprogramming large language models","author":"Jin","year":"2024"},{"key":"10.1016\/j.ipm.2026.104999_b19","series-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2022"},{"key":"10.1016\/j.ipm.2026.104999_b20","series-title":"The 41st international ACM SIGIR conference on research and development in information retrieval","first-page":"95","article-title":"Modeling long- and short-term temporal patterns with deep neural networks","author":"Lai","year":"2018"},{"key":"10.1016\/j.ipm.2026.104999_b21","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume":"Vol. 32","author":"Li","year":"2019"},{"issue":"3","key":"10.1016\/j.ipm.2026.104999_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.104025","article-title":"Financial risk assessment of imbalanced data based on nonlinear causal time-series network","volume":"62","author":"Li","year":"2025","journal-title":"Information Processing & Management"},{"issue":"4","key":"10.1016\/j.ipm.2026.104999_b23","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","article-title":"Temporal fusion Transformers for interpretable multi-horizon time series forecasting","volume":"37","author":"Lim","year":"2021","journal-title":"International Journal of Forecasting"},{"key":"10.1016\/j.ipm.2026.104999_b24","series-title":"Proceedings of the 41st international conference on machine learning","first-page":"30211","article-title":"SparseTSF: Modeling long-term time series forecasting with 1k parameters","volume":"Vol. 235","author":"Lin","year":"2024"},{"key":"10.1016\/j.ipm.2026.104999_b25","series-title":"International conference on learning representations","article-title":"iTransformer: Inverted transformers are effective for time series forecasting","author":"Liu","year":"2024"},{"key":"10.1016\/j.ipm.2026.104999_b26","series-title":"International conference on learning representations","article-title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","author":"Liu","year":"2022"},{"key":"10.1016\/j.ipm.2026.104999_b27","first-page":"5816","article-title":"SCINet: Time series modeling and forecasting with sample convolution and interaction","volume":"Vol. 35","author":"Liu","year":"2022"},{"key":"10.1016\/j.ipm.2026.104999_b28","series-title":"Learning sparse neural networks through L0 regularization","author":"Louizos","year":"2018"},{"issue":"4","key":"10.1016\/j.ipm.2026.104999_b29","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1109\/PROC.1975.9792","article-title":"Linear prediction: A tutorial review","volume":"63","author":"Makhoul","year":"1975","journal-title":"Proceedings of the IEEE"},{"key":"10.1016\/j.ipm.2026.104999_b30","series-title":"International conference on learning representations","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","author":"Nie","year":"2023"},{"key":"10.1016\/j.ipm.2026.104999_b31","series-title":"International conference on learning representations","article-title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","author":"Oreshkin","year":"2020"},{"key":"10.1016\/j.ipm.2026.104999_b32","series-title":"Proceedings of the twenty-sixth international joint conference on artificial intelligence","first-page":"2627","article-title":"A dual-stage attention-based recurrent neural network for time series prediction","author":"Qin","year":"2017"},{"key":"10.1016\/j.ipm.2026.104999_b33","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1162\/tacl_a_00353","article-title":"Efficient content-based sparse attention with routing Transformers","volume":"9","author":"Roy","year":"2021","journal-title":"Transactions of the Association for Computational Linguistics"},{"issue":"3","key":"10.1016\/j.ipm.2026.104999_b34","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":"Salinas","year":"2020","journal-title":"International Journal of Forecasting"},{"key":"10.1016\/j.ipm.2026.104999_b35","series-title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer","author":"Shazeer","year":"2017"},{"key":"10.1016\/j.ipm.2026.104999_b36","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1016\/j.neunet.2023.06.044","article-title":"GBT: Two-stage Transformer framework for non-stationary time series forecasting","volume":"165","author":"Shen","year":"2023","journal-title":"Neural Networks"},{"issue":"10","key":"10.1016\/j.ipm.2026.104999_b37","doi-asserted-by":"crossref","first-page":"2685","DOI":"10.1109\/78.790651","article-title":"Universal linear prediction by model order weighting","volume":"47","author":"Singer","year":"1999","journal-title":"IEEE Transactions on Signal Processing"},{"issue":"1","key":"10.1016\/j.ipm.2026.104999_b38","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the performance of prediction models","volume":"21","author":"Steyerberg","year":"2010","journal-title":"Epidemiology"},{"key":"10.1016\/j.ipm.2026.104999_b39","series-title":"2024 international joint conference on neural networks","first-page":"1","article-title":"FocusLearn: Fully-interpretable, high-performance modular neural networks for time series","author":"Su","year":"2024"},{"key":"10.1016\/j.ipm.2026.104999_b40","series-title":"FreDo: Frequency domain-based long-term time series forecasting","author":"Sun","year":"2022"},{"issue":"3","key":"10.1016\/j.ipm.2026.104999_b41","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1016\/j.ipm.2018.11.004","article-title":"Using time-series analysis to predict disease counts with structural trend changes","volume":"56","author":"Talaei-Khoei","year":"2019","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104999_b42","first-page":"12640","article-title":"Unlocking the power of patch: Patch-based MLP for long-term time series forecasting","volume":"Vol. 39","author":"Tang","year":"2025"},{"key":"10.1016\/j.ipm.2026.104999_b43","series-title":"Sparse sinkhorn attention","author":"Tay","year":"2020"},{"issue":"1","key":"10.1016\/j.ipm.2026.104999_b44","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"Journal of the Royal Statistical Society. Series B. Statistical Methodology"},{"key":"10.1016\/j.ipm.2026.104999_b45","first-page":"5998","article-title":"Attention is all you need","volume":"Vol. 30","author":"Vaswani","year":"2017"},{"issue":"1","key":"10.1016\/j.ipm.2026.104999_b46","doi-asserted-by":"crossref","DOI":"10.1038\/ctg.2013.19","article-title":"A primer on predictive models","volume":"5","author":"Waljee","year":"2014","journal-title":"Clinical and Translational Gastroenterology"},{"key":"10.1016\/j.ipm.2026.104999_b47","first-page":"21375","article-title":"FilterTS: Comprehensive frequency filtering for multivariate time series forecasting","volume":"Vol. 39","author":"Wang","year":"2025"},{"key":"10.1016\/j.ipm.2026.104999_b48","series-title":"International conference on learning representations","article-title":"MICN: Multi-scale local and global context modeling for long-term series forecasting","author":"Wang","year":"2023"},{"key":"10.1016\/j.ipm.2026.104999_b49","first-page":"26277","article-title":"RI-Loss: A learnable residual-informed loss for time series forecasting","volume":"Vol. 40","author":"Wang","year":"2026"},{"key":"10.1016\/j.ipm.2026.104999_b50","first-page":"1","article-title":"Deep time series models: A comprehensive survey and benchmark","author":"Wang","year":"2026","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.ipm.2026.104999_b51","first-page":"26480","article-title":"CometNet: Contextual motif-guided long-term time series forecasting","volume":"Vol. 40","author":"Wang","year":"2026"},{"key":"10.1016\/j.ipm.2026.104999_b52","series-title":"A multi-horizon quantile recurrent forecaster","author":"Wen","year":"2018"},{"key":"10.1016\/j.ipm.2026.104999_b53","first-page":"28169","article-title":"ETSformer: Exponential smoothing Transformers for time-series forecasting","volume":"Vol. 35","author":"Woo","year":"2022"},{"key":"10.1016\/j.ipm.2026.104999_b54","series-title":"International conference on learning representations","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","author":"Wu","year":"2023"},{"key":"10.1016\/j.ipm.2026.104999_b55","article-title":"Adversarial sparse transformer for time series forecasting","volume":"Vol. 33","author":"Wu","year":"2020"},{"key":"10.1016\/j.ipm.2026.104999_b56","first-page":"22419","article-title":"Autoformer: Decomposition Transformers with auto-correlation for long-term series forecasting","volume":"Vol. 34","author":"Wu","year":"2021"},{"key":"10.1016\/j.ipm.2026.104999_b57","first-page":"11121","article-title":"Are Transformers effective for time series forecasting?","volume":"Vol. 37","author":"Zeng","year":"2023"},{"key":"10.1016\/j.ipm.2026.104999_b58","series-title":"Effectively modeling time series with simple discrete state spaces","author":"Zhang","year":"2023"},{"key":"10.1016\/j.ipm.2026.104999_b59","series-title":"International conference on learning representations","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","author":"Zhang","year":"2023"},{"issue":"7","key":"10.1016\/j.ipm.2026.104999_b60","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.14778\/3654621.3654637","article-title":"A multi-scale decomposition MLP-Mixer for time series analysis","volume":"17","author":"Zhong","year":"2024","journal-title":"Proceedings of the VLDB Endowment"},{"key":"10.1016\/j.ipm.2026.104999_b61","series-title":"Proceedings of the 39th international conference on machine learning","first-page":"27268","article-title":"FEDformer: Frequency enhanced decomposed Transformer for long-term series forecasting","volume":"Vol. 162","author":"Zhou","year":"2022"},{"key":"10.1016\/j.ipm.2026.104999_b62","series-title":"Deep latent state space models for time-series generation","author":"Zhou","year":"2023"},{"key":"10.1016\/j.ipm.2026.104999_b63","first-page":"11106","article-title":"Informer: Beyond efficient Transformer for long sequence time-series forecasting","volume":"Vol. 35","author":"Zhou","year":"2021"}],"container-title":["Information Processing &amp; Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326003900?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326003900?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T21:54:57Z","timestamp":1783374897000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457326003900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2027,1]]}},"alternative-id":["S0306457326003900"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104999","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Disentangled temporal modeling with sparse convolutions for time series forecasting","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104999","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104999"}}