MambaFormer-MOE: Mamba-Transformer-based Mixture-of-Experts for Time Series Prediction

Authors: Weijian Li, Han Liu
Primary area: learning on time series and dynamical systems
Keywords: mamba, transformer, mixture-of-experts, time series prediction

Abstract:
We propose the MambaFormer-MOE, a mamba-based mixture-of-experts (MOEs) model for multivariate time series prediction. There are three major features of our model. 1. We propose a temporal modeling module based-on the Mamba architecture to model temporal correlations with linear complexity. 2. We propose a cross-variate correlation modeling mechanism based-on self-attention to equip Mamba with multivariate time series prediction capability. 3. We propose a MOE mechanism that has experts that specialize in mixing the variates in different ways. It makes the model generalizable to multivariate time series from different domains. Our empirical results demonstrate that our model has SOTA prediction performance on various multivariate time series datasets.

Decision: desk reject

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