Dual-Path Delay-Aware Mamba Model Enhances Multivariate Time Series Analysis

Global: Dual-Path Delay-Aware Mamba Model Enhances Multivariate Time Series Analysis

Researchers introduced DeMa, a dual‑path delay‑aware Mamba backbone, to improve the scalability and effectiveness of multivariate time series (MTS) analysis. The model preserves Mamba’s linear‑time computational advantage while addressing key shortcomings of both Transformer‑based architectures and vanilla Mamba implementations. The work was submitted to arXiv in January 2026 and is publicly accessible, aiming to support a wide range of intelligent applications that rely on accurate and efficient MTS processing.

Background

Transformer models have become dominant for MTS tasks because of their ability to capture pairwise dependencies, yet they suffer from quadratic computational complexity and high memory demands. Recently, the Mamba architecture emerged as a linear‑time alternative with strong expressive power, but its direct application to MTS has proven suboptimal.

Limitations of Existing Approaches

Three primary challenges limit vanilla Mamba in the MTS context: (i) it lacks explicit mechanisms for cross‑variate modeling, (ii) it struggles to disentangle intra‑series temporal dynamics from inter‑series interactions, and (iii) it does not adequately represent latent time‑lag interaction effects. These gaps reduce its effectiveness across diverse forecasting, imputation, and classification tasks.

DeMa Architecture

DeMa addresses the identified challenges through three innovations. First, it decomposes the MTS problem into separate intra‑series temporal dynamics and inter‑series interaction components. Second, a temporal path equipped with a Mamba‑SSD module captures long‑range dynamics within each individual series, enabling series‑independent, parallel computation. Third, a variate path featuring a Mamba‑DALA module incorporates delay‑aware linear attention to model cross‑variate dependencies efficiently.

Temporal Path Details

The temporal path’s Mamba‑SSD module processes each series independently, preserving linear computational complexity while extracting long‑range temporal patterns. This design allows simultaneous processing of multiple series without inter‑series interference, reducing memory overhead.

Variate Path Details

The variate path’s Mamba‑DALA module introduces delay‑aware linear attention, which explicitly models the timing offsets between variables. This mechanism captures latent time‑lag interactions that are critical for accurate multivariate forecasting and anomaly detection.

Experimental Validation

Extensive experiments were conducted on five representative MTS tasks: long‑ and short‑term forecasting, data imputation, anomaly detection, and series classification. Across all benchmarks, DeMa achieved state‑of‑the‑art performance while delivering markedly lower computational costs compared to Transformer‑based baselines and vanilla Mamba.

Implications and Future Work

By maintaining linear complexity and enhancing cross‑variate modeling, DeMa positions itself as a viable solution for large‑scale, long‑horizon MTS applications in sectors such as finance, healthcare, and IoT. Future research may explore adapting the architecture to other sequence‑modeling domains, refining the delay‑aware attention mechanism, and integrating DeMa into end‑to‑end pipelines.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.