{"type":"ScholarlyArticle","title":"AdaMixT: Adaptive Weighted Mixture of Multi-Scale Expert Transformers for Time Series Forecasting","authors":[{"name":"Huanyao Zhang","slug":"huanyao-zhang","given":"Huanyao","family":"Zhang","affiliation":""},{"name":"Jiaye Lin","slug":"jiaye-lin","given":"Jiaye","family":"Lin","affiliation":""},{"name":"Wentao Zhang","slug":"wentao-zhang","given":"Wentao","family":"Zhang","affiliation":""},{"name":"Haitao Yuan","slug":"haitao-yuan","given":"Haitao","family":"Yuan","affiliation":""},{"name":"Guoliang Li","slug":"guoliang-li","given":"Guoliang","family":"Li","affiliation":""}],"abstract":"Multivariate time series forecasting involves predicting future values based on historical observations. However, existing approaches primarily rely on predefined single-scale patches or lack effective mechanisms for multi-scale feature fusion. These limitations hinder them from fully capturing the complex patterns inherent in time series, leading to constrained performance and insufficient generalizability. To address these challenges, we propose a novel architecture named Adaptive Weighted Mixture of Multi-Scale Expert Transformers (AdaMixT). Specifically, AdaMixT introduces various patches and leverages both General Pre-trained Models (GPM) and Domain-specific Models (DSM) for multi-scale feature extraction. To accommodate the heterogeneity of temporal features, AdaMixT incorporates a gating network that dynamically allocates weights among different experts, enabling more accurate predictions through adaptive multi-scale fusion. Comprehensive experiments on eight widely used benchmarks, including Weather, Traffic, Electricity, ILI, and four ETT datasets, consistently demonstrate the effectiveness of AdaMixT in real-world scenarios.","doi":"10.24963/ijcai.2025/404","url":"https://ijcai.org/proceedings/2025/404","pdf":"https://ijcai.org/proceedings/2025/0404.pdf","markdown":"https://ijcai.org/proceedings/2025/404.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/404","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":404,"pages":{"first":3633,"last":3641},"keywords":["Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}