Tribe Graph Enhanced Bidirectional Mamba for Multivariate Time Series Forecasting

Abstract

In multivariate time series forecasting, transformer-based methods have gained attention for their ability to capture complex dependencies and are often integrated with graph neural networks to improve forecasting performance. However, these approaches are computationally intensive. Mamba, a more computationally efficient architecture, has emerged as a potential solution. Despite its efficiency, Mamba fails to capture interactions across time and dependencies between variables. Furthermore, the graph structures used in most existing methods to model inter-series relationships primarily capture correlations between pairs of variables, neglecting the similarity of patterns among different variable series. In light of this, we propose a Tribe Graph enhanced Bidirectional Mamba for multivariate time series forecasting (TGBiMamba). This approach leverages bidirectional Mamba as the backbone to capture intra-series interactions and employs a designed tribe graph structure to capture inter-series intrinsic similarities. Extensive experiments on various public datasets and a proprietary industrial dataset highlight the superiority of our method.