{
  "annotation_id": "ae23bddc-6f1f-4d8b-93bf-51b135122470",
  "date_created": "2026-02-17T05:53:05.047000Z",
  "date_modified": "2026-02-17T05:53:05.047000Z",
  "file_hash": "104e9fe73b5d9f95cbfa3691953942294355c1649cc845e3abe47e2391f52fa1",
  "private": false,
  "record": {
    "abstract": "Accurate and efficient multivariate time series (MTS) analysis is increasingly critical for a wide range of intelligent applications. 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 in long-term and 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 delay-aware Mamba backbone. DeMa preserves Mamba\u0027s linear-complexity advantage while substantially improving its suitability for MTS settings. Specifically, DeMa introduces three key innovations: (i) it decomposes the MTS into intra-series temporal dynamics and inter-series interactions; (ii) it develops a temporal path with a Mamba-SSD module to capture long-range dynamics within each individual series, enabling series-independent, parallel computation; and (iii) it designs a variate path with a Mamba-DALA module that integrates delay-aware linear attention to model cross-variate dependencies. 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.",
    "arxiv_id": "2601.05527",
    "authors": [
      "Rui An",
      "Haohao Qu",
      "Wenqi Fan",
      "Xuequn Shang",
      "Qing Li"
    ],
    "categories": [
      "cs.LG",
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis",
    "url": "https://arxiv.org/abs/2601.05527",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "96147d1d-9a0c-4acf-98a5-6f4bc6668dbf",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}