MoHETS: Long-term Time Series Forecasting with Mixture-of-Heterogeneous-Experts

ArXi:2601.21866v2 Announce Type: replace-cross Real-world multivariate time series can exhibit intricate multi-scale structures, including global trends, local periodicities, and non-stationary regimes, which makes long-horizon forecasting challenging. Although sparse Mixture-of-Experts (MoE) approaches improve scalability and specialization, they typically rely on homogeneous MLP experts that poorly capture the diverse temporal dynamics of time series data.