App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 7

Multivariate time series anomaly detection based on time-frequency domain reconstruction fusion

Liu Mengxuan1
Ping Peng2
Xu Yang1
1. School of Computer Science, Nanjing University of Information Science &Technology, Nanjing 210000, China
2. School of Transportation and Civil Engineering, Nantong University, Nantong Jiangsu 226000, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0465

Abstract

Multivariate time series anomaly detection is crucial for ensuring the secure and stable operation of systems. However, most existing methods modelled data solely in either the time or frequency domain, making it difficult to simultaneously capture transient anomalies and deviations in periodic patterns, thereby limiting detection accuracy. To address this, this paper proposed DDRF, a multivariate time series anomaly detection model based on time-frequency domain reconstruction and fusion. The model employed a parallel dual-branch architecture to synergistically leverage the complementary characteristics of time and frequency domain information. The time branch designed a hierarchical dilated causal convolutional network with exponentially expanding receptive fields to construct a multi-scale time feature pyramid, integrating microscopic instantaneous fluctuations from the bottom up into long-term dynamic trends. The frequency branch adopted a two-stage sparsification framework, progressively refining frequency-domain features along both the variable and frequency component dimensions to focus on key frequency-domain characteristics and capture multivariate collaborative anomalies. Furthermore, to overcome the limitations of traditional single-domain modeling, an adaptive gated fusion mechanism dynamically weighted and integrated the reconstructed results from the dual domains. DDRF performed anomaly detection based on joint reconstruction errors. Experimental results on four public datasets demonstrate that the proposed model outperforms mainstream baseline models such as Anomaly Transformer in both Aff-F1 and AUC-ROC metrics.

Foundation Support

国家自然科学基金资助项目(U2433216)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0465
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-23] Accepted Paper

Cite This Article

刘萌轩, 平鹏, 徐扬. 基于时频域重构融合的多变量时序异常检测 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0465. (Liu Mengxuan, Ping Peng, Xu Yang. Multivariate time series anomaly detection based on time-frequency domain reconstruction fusion [J]. Application Research of Computers, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0465. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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