Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting
arXiv:2605.20678v1 Announce Type: new Abstract: Non-stationary time series forecasting is challenged by evolving distribution shifts that static models struggle to capture. While Mixture-of-Experts (MoE) architectures offer a promising paradigm for decoupling complex drift patterns, existing approaches are limited by fixed expert pools and memoryless routing, hampering their ability to adapt to abrupt regime shifts. To address this, we propose Dynamic TMoE, a framework that unifies architectural evolution with temporal continuity during learning phase. By detecting distribution shifts via Maxi
The increasing prevalence of dynamic, non-stationary data in real-world applications is driving demand for more adaptive forecasting models.
This development allows for more robust and accurate predictions in complex, evolving systems, which is crucial for decision-making in various critical sectors.
Traditional static forecasting models are becoming less effective, pushing the need for dynamic architectures that can autonomously adapt to significant distribution shifts.
- · AI researchers and developers
- · Industries reliant on time series forecasting (finance, logistics, energy)
- · Cloud providers offering adaptive AI services
- · Developers of static forecasting models
- · Organizations relying on outdated predictive analytics
Improved accuracy in predicting future events in highly volatile environments.
Faster and more automated responses to emergent systemic changes, reducing human intervention.
Enhanced resilience and efficiency across critical infrastructure and economic operations due to better forecasting.
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Read at arXiv cs.LG