Causal Mamba for Multivariate Time Series Forecasting

Multivariate time-series forecasting (MTSF) is essential for decision-making in various domains. As modern systems grow in complexity, effectively modeling inter-variable dependency has become increasingly important. Mamba, a recent state space model known for its ability to capture long-range dependencies, has also been adapted to model correlations among variables. However, vanilla-Mamba ignores causal direction/sequential order between variables, which limits their effectiveness in discovering complex dependency patterns. To address this, we propose Causal Mamba, a novel Mamba model for MTSF tasks. Specifically, we incorporate a Granger causality and topological sorting method to extract the causal order of variables. Subsequently, we employ the causal order information to modify vanilla-Mamba into Dynamic Mamba, which captures directional inter-variable dependency. Then we use TCN and FFN to model temporal dependency. Comprehensive experiments conducted on six real-world datasets demonstrate the efficacy of our model in improving forecasting performance, and validate the effectiveness of causal order of variables compared to vanilla-Mamba.

Recommended citation: * (2025). Causal Mamba for Multivariate Time Series Forecasting. (Submitted)