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

Time series anomaly detection based on time-frequency domain attention and local normalization loss

Li Youmeng
Kong Jun
Jiang Min
School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0451

Abstract

To address the challenges in time series anomaly detection where existing methods struggled to effectively handle scale variations caused by data non-stationarity and often overlooked in-depth exploration of frequency-domain information, this paper proposed a Time-Frequency Domain Attention mechanism with Local Normalization Loss (TFDA-LNL) . The method primarily consisted of a Time-Frequency Domain Attention module and a Local Normalization Loss module. The Time-Frequency Domain Attention module captured trend features through temporal attention and extracted seasonal features using frequency-domain attention, while an instance contrastive loss was incorporated to promote effective fusion of temporal and frequency information. The Local Normalization Loss module applied local standardization to the input and reconstructed time windows before calculating the reconstruction loss, which not only highlighted anomalies within the time window but also eliminated noise interference, thereby mitigating scale differences caused by data non-stationarity. Experimental results on five public datasets demonstrated that TFDA-LNL significantly outperformed existing state-of-the-art models in both F1 and AUC metrics, achieving average improvements of 1.66% and 4.35%, respectively. The code was made available at: https: //github. com/jn-liyoumeng/TFDA-LNL.

Foundation Support

国家自然科学基金资助项目(62371209,62371208)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0451
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-18] Accepted Paper

Cite This Article

李佑猛, 孔军, 蒋敏. 基于时频域注意力与局部归一化损失的时间序列异常检测 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0451. (Li Youmeng, Kong Jun, Jiang Min. Time series anomaly detection based on time-frequency domain attention and local normalization loss [J]. Application Research of Computers, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0451. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)