DeMa: dual-path delay-aware Mamba for efficient multivariate time series analysis

Abstract Accurate and efficient multivariate time series (MTS) analysis is increasingly critical for a wide range of intelligent applications, including traffic forecasting, anomaly detection for industrial maintenance, and trajectory classification for health monitoring. Within this realm, Transformers have emerged as the predominant architecture due to their strong ability to capture pairwise dependencies. However, Transformer-based models suffer from quadratic computational complexity and high memory overhead, limiting their scalability and practical deployment for long-term, large-scale MTS modeling. Recently, Mamba has emerged as a promising linear-time alternative with high expressiveness. Nevertheless, directly applying vanilla Mamba to MTS remains suboptimal due to three key limitations: (i) the lack of explicit cross-variate modeling, (ii) difficulty in disentangling the entangled intra-series temporal dynamics and inter-series interactions, and (iii) insufficient modeling of latent time-lag interaction effects. These issues constrain its effectiveness across diverse MTS tasks. To address these challenges, we propose DeMa , a dual-path De lay-Aware Ma mba backbone for efficient and effective MTS analysis. DeMa preserves Mamba’s linear-complexity advantage while substantially improving its suitability for multivariate settings. Specifically, DeMa introduces three key innovations: (i) it decomposes the MTS context into intra-series temporal dynamics and inter-series interactions and learns them via two dedicated paths; (ii) it develops a temporal path with a Mamba-SSD module to capture long-range dynamics within each series, accommodating variable-length inputs and enabling series-independent, parallel computation while maintaining linear complexity; and (iii) it designs a variate path with a Mamba-DALA module that integrates delay-aware linear attention to model cross-variate dependencies, enhancing fine-grained, delay-sensitive dependency learning. Extensive experiments on five representative tasks, long- and short-term forecasting, data imputation, anomaly detection, and series classification, demonstrate that DeMa achieves state-of-the-art performance while delivering remarkable computational efficiency.