---
title: "AdaMixT: Adaptive Weighted Mixture of Multi-Scale Expert Transformers for Time Series Forecasting"
authors:
  - "Huanyao Zhang"
  - "Jiaye Lin"
  - "Wentao Zhang"
  - "Haitao Yuan"
  - "Guoliang Li"
doi: "10.24963/ijcai.2025/404"
url: "https://ijcai.org/proceedings/2025/404"
pdf: "https://ijcai.org/proceedings/2025/0404.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "3633-3641"
track: "Main Track"
keywords:
  - "Data Mining: DM: Mining spatial and/or temporal data"
---

# AdaMixT: Adaptive Weighted Mixture of Multi-Scale Expert Transformers for Time Series Forecasting

Huanyao Zhang, Jiaye Lin, Wentao Zhang, Haitao Yuan, Guoliang Li

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 3633-3641. DOI: [10.24963/ijcai.2025/404](https://doi.org/10.24963/ijcai.2025/404)

## 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.