Computer Science > Machine Learning
arXiv:2603.09062 (cs)
[Submitted on 10 Mar 2026]
Title:Dynamic Multi-period Experts for Online Time Series Forecasting
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Abstract:Online Time Series Forecasting (OTSF) requires models to continuously adapt to concept drift. However, existing methods often treat concept drift as a monolithic phenomenon. To address this limitation, we first redefine concept drift by categorizing it into two distinct types: Recurring Drift, where previously seen patterns reappear, and Emergent Drift, where entirely new patterns emerge. We then propose DynaME (Dynamic Multi-period Experts), a novel hybrid framework designed to effectively address this dual nature of drift. For Recurring Drift, DynaME employs a committee of specialized experts that are dynamically fitted to the most relevant historical periodic patterns at each time step. For Emergent Drift, the framework detects high-uncertainty scenarios and shifts reliance to a stable, general expert. Extensive experiments on several benchmark datasets and backbones demonstrate that DynaME effectively adapts to both concept drifts and significantly outperforms existing baselines.
| Comments: | |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.09062 [cs.LG] |
| (or arXiv:2603.09062v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09062
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View a PDF of the paper titled Dynamic Multi-period Experts for Online Time Series Forecasting, by Seungha Hong and 4 other authors
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