Unlocking the Power of Mixture-of-Experts for Task-Aware Time Series Analytics

• URL: http://arxiv.org/abs/2509.22279v2
• Date: Mon, 20 Oct 2025 06:08:41 GMT
• Title: Unlocking the Power of Mixture-of-Experts for Task-Aware Time Series Analytics
• Authors: Xingjian Wu, Zhengyu Li, Hanyin Cheng, Xiangfei Qiu, Jilin Hu, Chenjuan Guo, Bin Yang,
• Abstract summary: Time Series Analysis is widely used in various real-world applications such as weather forecasting, financial fraud detection, imputation for missing data in IoT systems, and classification for action recognization.<n>MoE, as a powerful architecture, still falls short in adapting to versatile tasks in time series analytics due to its task-agnostic router and the lack of capability in modeling channel correlations.<n>We propose a novel, general MoE-based time series framework called PatchMoE to support the intricate knowledge'' utilization for distinct tasks, thus task-aware.
• Score: 18.97715342585514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time Series Analysis is widely used in various real-world applications such as weather forecasting, financial fraud detection, imputation for missing data in IoT systems, and classification for action recognization. Mixture-of-Experts (MoE), as a powerful architecture, though demonstrating effectiveness in NLP, still falls short in adapting to versatile tasks in time series analytics due to its task-agnostic router and the lack of capability in modeling channel correlations. In this study, we propose a novel, general MoE-based time series framework called PatchMoE to support the intricate ``knowledge'' utilization for distinct tasks, thus task-aware. Based on the observation that hierarchical representations often vary across tasks, e.g., forecasting vs. classification, we propose a Recurrent Noisy Gating to utilize the hierarchical information in routing, thus obtaining task-sepcific capability. And the routing strategy is operated on time series tokens in both temporal and channel dimensions, and encouraged by a meticulously designed Temporal \& Channel Load Balancing Loss to model the intricate temporal and channel correlations. Comprehensive experiments on five downstream tasks demonstrate the state-of-the-art performance of PatchMoE.

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