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arXiv Machine Learning By Btissame El Mahtout, Florian Ziel

Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration

Read the original on arXiv Machine Learning →

The paper introduces an adaptive Mixture-of-Experts (MoE) framework for time series forecasting that incorporates expert-specific losses to give each expert a direct learning signal independent of gating weights. The overall objective combines base forecasting loss with these expert losses, encouraging experts to specialize on different temporal segments. A partial online learning strategy is added for efficient incremental updates, and experiments on economic, tourism, and energy datasets show the method outperforms state‑of‑the‑art neural models and foundation models, with ablation studies confirming the benefit of expert loss integration.

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