{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:23:37Z","timestamp":1773804217712,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"33","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Multivariate time series forecasting underpins applications in finance, meteorology, and industrial operations. Yet two persistent hurdles remain: (i) models typically choose between Channel\u2013Independent (CI) and Channel\u2013Mixed (CM) formulations\u2014each with distinct strengths\u2014leading to large performance variance across datasets; and (ii) short-term dynamics and long-term trends are hard to model jointly, making it difficult to capture both transient bursts and periodic patterns. We propose FusionTimePatch (FTP), a purely MLP-driven, lightweight framework composed of three modules: (1) Dual-View Global\u2013Local Fusion (Dual-GLF), which runs CI and CM views in parallel and employs multi-scale patch recursion to adaptively adjust the look-back window, thereby coupling global tendencies with local details; (2) Channel Enhancement (CE), which adaptively identifies and amplifies salient channel signals and diffuses them to others, improving sensitivity to abrupt events and latent drivers; and (3) a Linear Fusion layer, which unifies Dual-GLF and CE outputs to strengthen cross-view interactions and enhance robustness. Extensive experiments on multiple public benchmarks show FTP consistently surpasses state-of-the-art counterparts in both accuracy and efficiency, offering a scalable new paradigm for multichannel forecasting. Code and datasets are publicly available at https:\/\/github.com\/Zhveh7\/FTP.<\/jats:p>","DOI":"10.1609\/aaai.v40i33.40072","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:20:18Z","timestamp":1773800418000},"page":"28427-28436","source":"Crossref","is-referenced-by-count":0,"title":["Unifying Channel Independence and Mixing: Multi-Scale Patch Recursion for Global\u2013Local Representation Synergy in Multivariate Time Series Forecasting"],"prefix":"10.1609","volume":"40","author":[{"given":"Wenhao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changxia","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenhao","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoxiong","family":"Pang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/40072\/44033","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/40072\/44033","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:20:18Z","timestamp":1773800418000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/40072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"33","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i33.40072","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}