{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T17:56:46Z","timestamp":1781459806509,"version":"3.54.1"},"reference-count":58,"publisher":"Elsevier BV","issue":"8","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"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":[[2026,12]]},"DOI":"10.1016\/j.ipm.2026.104942","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T10:18:04Z","timestamp":1781000284000},"page":"104942","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["CA-DE: Heterogeneous component-aware and antagonistic dependency-enhanced dual paths for long-term time series forecasting"],"prefix":"10.1016","volume":"63","author":[{"given":"Xiaoyue","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9803-2745","authenticated-orcid":false,"given":"Weimin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangfang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanke","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ipm.2026.104942_b1","series-title":"Time series analysis: forecasting and control","author":"Box","year":"2015"},{"key":"10.1016\/j.ipm.2026.104942_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126849","article-title":"MSPatch: A multi-scale patch mixing framework for multivariate time series forecasting","volume":"273","author":"Cao","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104942_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108218","article-title":"Financial time series forecasting with multi-modality graph neural network","volume":"121","author":"Cheng","year":"2022","journal-title":"Pattern Recognition"},{"issue":"1","key":"10.1016\/j.ipm.2026.104942_b4","first-page":"3","article-title":"STL: A seasonal-trend decomposition","volume":"6","author":"Cleveland","year":"1990","journal-title":"J. Off. Stat"},{"key":"10.1016\/j.ipm.2026.104942_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114745","article-title":"Global-local coherency contrastive learning for context-aware time series forecasting","volume":"331","author":"Ding","year":"2026","journal-title":"Knowledge-Based Systems"},{"issue":"4","key":"10.1016\/j.ipm.2026.104942_b6","doi-asserted-by":"crossref","first-page":"7424","DOI":"10.1016\/j.eswa.2008.09.040","article-title":"A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model","volume":"36","author":"Egrioglu","year":"2009","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104942_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112825","article-title":"SDR-GNN: Spectral domain reconstruction graph neural network for incomplete multimodal learning in conversational emotion recognition","volume":"309","author":"Fu","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104942_b8","series-title":"Advances in neural information processing systems","first-page":"64145","article-title":"SOFTS: Efficient multivariate time series forecasting with series-core fusion","volume":"37","author":"Han","year":"2024"},{"key":"10.1016\/j.ipm.2026.104942_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111037","article-title":"Parallel multi-scale dynamic graph neural network for multivariate time series forecasting","volume":"158","author":"Hou","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.ipm.2026.104942_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.122709","article-title":"Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data","volume":"359","author":"Hu","year":"2024","journal-title":"Applied Energy"},{"key":"10.1016\/j.ipm.2026.104942_b11","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"17359","article-title":"Adaptive multi-scale decomposition framework for time series forecasting","volume":"vol. 39","author":"Hu","year":"2025"},{"key":"10.1016\/j.ipm.2026.104942_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113971","article-title":"mLANet: An efficient recurrent neural network for long-term time series forecasting","volume":"325","author":"Jiang","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104942_b13","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.2026.104942_b14","series-title":"The 3rd international conference for learning representations","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"issue":"1","key":"10.1016\/j.ipm.2026.104942_b15","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1186\/s40537-024-01001-9","article-title":"Cmmamba: channel mixing mamba for time series forecasting","volume":"11","author":"Li","year":"2024","journal-title":"Journal of Big Data"},{"key":"10.1016\/j.ipm.2026.104942_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110965","article-title":"Toeformer: Temporal order enhanced transformer for time series forecasting","volume":"155","author":"Li","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.ipm.2026.104942_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.05.028","article-title":"EA-LSTM: Evolutionary attention-based LSTM for time series prediction","volume":"181","author":"Li","year":"2019","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104942_b18","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.eswa.2017.04.013","article-title":"Random forests-based extreme learning machine ensemble for multi-regime time series prediction","volume":"83","author":"Lin","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104942_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114952","article-title":"Faformer: A frequency-aware transformer with adaptive energy decomposition for multivariate time series forecasting","volume":"332","author":"Liu","year":"2026","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104942_b20","series-title":"itransformer: Inverted transformers are effective for time series forecasting","author":"Liu","year":"2024"},{"key":"10.1016\/j.ipm.2026.104942_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106081","article-title":"CNN-FCM: System modeling promotes stability of deep learning in time series prediction","volume":"203","author":"Liu","year":"2020","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104942_b22","series-title":"International Conference on Learning Representations","article-title":"Timer-xl: long-context transformers for unified time series forecasting","author":"Liu","year":"2025"},{"key":"10.1016\/j.ipm.2026.104942_b23","series-title":"AAAI","first-page":"18780","article-title":"TimeCMA: Towards LLM-empowered multivariate time series forecasting via cross-modality alignment","author":"Liu","year":"2025"},{"key":"10.1016\/j.ipm.2026.104942_b24","series-title":"The eleventh 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.2026.104942_b25","series-title":"ICLR 2020","article-title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","author":"Oreshkin","year":"2019"},{"key":"10.1016\/j.ipm.2026.104942_b26","series-title":"Advances in neural information processing systems","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019"},{"key":"10.1016\/j.ipm.2026.104942_b27","series-title":"Proceedings of the 31st ACM SIGKDD conference on knowledge discovery and data mining v.1","first-page":"1185","article-title":"DUET: Dual clustering enhanced multivariate time series forecasting","author":"Qiu","year":"2025"},{"key":"10.1016\/j.ipm.2026.104942_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106484","article-title":"ECG-net: A deep LSTM autoencoder for detecting anomalous ECG","volume":"124","author":"Roy","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"2","key":"10.1016\/j.ipm.2026.104942_b29","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MCI.2009.932254","article-title":"Time series prediction using support vector machines: A survey","volume":"4","author":"Sapankevych","year":"2009","journal-title":"IEEE Computational Intelligence Magazine"},{"key":"10.1016\/j.ipm.2026.104942_b30","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"20601","article-title":"Xpatch: Dual-stream time series forecasting with exponential seasonal-trend decomposition","volume":"vol. 39","author":"Stitsyuk","year":"2025"},{"issue":"5","key":"10.1016\/j.ipm.2026.104942_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104191","article-title":"MRLCD-A: Lag-aware alignment for multivariate time series forecasting in multiple scenarios","volume":"62","author":"Sun","year":"2025","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104942_b32","series-title":"Proceedings of the AAAI conference on artificial intelligence","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.2026.104942_b33","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104942_b34","series-title":"ICLR","article-title":"Timemixer++: A general time series pattern machine for universal predictive analysis","author":"Wang","year":"2025"},{"key":"10.1016\/j.ipm.2026.104942_b35","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"21090","article-title":"CSformer: Combining channel independence and mixing for robust multivariate time series forecasting","volume":"39","author":"Wang","year":"2025"},{"key":"10.1016\/j.ipm.2026.104942_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111463","article-title":"A lightweight multi-layer perceptron for efficient multivariate time series forecasting","volume":"288","author":"Wang","year":"2024","journal-title":"Knowledge-Based Systems"},{"issue":"2, Part A","key":"10.1016\/j.ipm.2026.104942_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104358","article-title":"Learning hierarchical time\u2013frequency representation for long-term time series forecasting","volume":"63","author":"Wang","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104942_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127362","article-title":"RLMamba: Integrating residual learning with mamba for long-term time series forecasting","volume":"278","author":"Wang","year":"2025","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.1016\/j.ipm.2026.104942_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104296","article-title":"AlignTime: Interperiodic phase alignment sampling for time-series forecasting","volume":"63","author":"Wang","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104942_b40","series-title":"The twelfth international conference on learning representations","article-title":"Timemixer: Decomposable multiscale mixing for time series forecasting","author":"Wang","year":"2024"},{"key":"10.1016\/j.ipm.2026.104942_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114900","article-title":"PCN: Patch segmentation convolutional networks in time series forecasting tasks","volume":"332","author":"Wang","year":"2026","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104942_b42","series-title":"The eleventh 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.2026.104942_b43","series-title":"Advances in neural information processing systems","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021"},{"key":"10.1016\/j.ipm.2026.104942_b44","series-title":"ICLR 2025: the thirteenth international conference on learning representations","article-title":"Time-moe: Billion-scale time series foundation models with mixture of experts","author":"Xiaoming","year":"2025"},{"key":"10.1016\/j.ipm.2026.104942_b45","series-title":"The twelfth international conference on learning representations","article-title":"Card: Channel aligned robust blend transformer for time series forecasting","author":"Xue","year":"2024"},{"key":"10.1016\/j.ipm.2026.104942_b46","series-title":"Proceedings of the thirty-third international joint conference on artificial intelligence","article-title":"Vcformer: variable correlation transformer with inherent lagged correlation for multivariate time series forecasting","author":"Yang","year":"2024"},{"key":"10.1016\/j.ipm.2026.104942_b47","series-title":"Advances in neural information processing systems","first-page":"76656","article-title":"Frequency-domain MLPs are more effective learners in time series forecasting","volume":"vol. 36","author":"Yi","year":"2023"},{"issue":"4","key":"10.1016\/j.ipm.2026.104942_b48","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1109\/TPAMI.2023.3331389","article-title":"Messages are never propagated alone: Collaborative hypergraph neural network for time-series forecasting","volume":"46","author":"Yin","year":"2024","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.ipm.2026.104942_b49","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s41019-020-00151-z","article-title":"A survey of traffic prediction: from spatio-temporal data to intelligent transportation","volume":"6","author":"Yuan","year":"2021","journal-title":"Data Science and Engineering"},{"key":"10.1016\/j.ipm.2026.104942_b50","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"11121","article-title":"Are transformers effective for time series forecasting?","volume":"vol. 37","author":"Zeng","year":"2023"},{"key":"10.1016\/j.ipm.2026.104942_b51","series-title":"ICDE\u20192026","article-title":"AutoHFormer: Efficient hierarchical autoregressive transformer for time series prediction","author":"Zhang","year":"2025"},{"key":"10.1016\/j.ipm.2026.104942_b52","series-title":"The eleventh international conference on learning representations","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","author":"Zhang","year":"2023"},{"issue":"3","key":"10.1016\/j.ipm.2026.104942_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104508","article-title":"Hierarchical prediction of irregular multivariate time series from a multi-granularity perspective","volume":"63","author":"Zhang","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104942_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114801","article-title":"RecMamba: Reconstruction-augmented dual-path mamba for time series forecasting","volume":"331","author":"Zhao","year":"2026","journal-title":"Knowledge-Based Systems"},{"issue":"12","key":"10.1016\/j.ipm.2026.104942_b55","doi-asserted-by":"crossref","first-page":"13902","DOI":"10.1109\/TCYB.2021.3121312","article-title":"An accurate GRU-based power time-series prediction approach with selective state updating and stochastic optimization","volume":"52","author":"Zheng","year":"2022","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.1016\/j.ipm.2026.104942_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2022.113046","article-title":"A hybrid framework for forecasting power generation of multiple renewable energy sources","volume":"172","author":"Zheng","year":"2023","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"10.1016\/j.ipm.2026.104942_b57","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.2026.104942_b58","series-title":"Proceedings of the AAAI conference on artificial intelligence","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:S030645732600333X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S030645732600333X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T17:08:28Z","timestamp":1781456908000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S030645732600333X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":58,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["S030645732600333X"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104942","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CA-DE: Heterogeneous component-aware and antagonistic dependency-enhanced dual paths for long-term 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.2026.104942","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":"104942"}}