Ada-MoF: Adaptive Mixture of Frequency-domain Experts for Time Series Forecasting

Multivariate time series forecasts are widely used, such as meteorology, transportation and financial forecasts. However, the dominant frequencies in time series may shift with the evolving spectral distribution of the data. Traditional Mixture of Experts (MoE) models, which employ a fixed number of experts, struggle to adapt to these changes, resulting in frequency coverage imbalance issue. Specifically, too few experts can lead to the overlooking of critical information, while too many can introduce noise. To this end, we propose Ada-MoF, an adaptive Mixture of Frequency-domain Experts model. Ada-MoF integrates univariate spectral intensity and cross-variable frequency response features to adaptively determine the number of experts, ensuring alignment with the input data's frequency distribution. This approach prevents both information loss due to an insufficient number of experts and noise contamination from an excess of experts. The experimental results show that our model achieves state-of-the-art performance on six public benchmarks with only 0.2 million parameters.