Computer Science ›› 2026, Vol. 53 ›› Issue (2): 161-169.doi: 10.11896/jsjkx.241200106

• Database & Big Data & Data Science • Previous Articles     Next Articles

Time-Frequency Attention Based Model for Time Series Anomaly Detection

XU Jingtao, YANG Yan, JIANG Yongquan   

  1. School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,ChinaEngineering Research Center of Sustainable Urban Intelligent Transportation,Ministry of Education,Chengdu 611756,China
  • Received:2024-12-16 Revised:2025-03-17 Published:2026-02-10
  • About author:XU Jingtao,born in 1999,postgraduate.His main research interest is time series anomaly detection and forecasting.
    YANG Yan,born in 1964,Ph.D,professor,Ph.D supervisor, is a member of CCF(No.06877D).Her main research interests include artificial intelligence,big data analysis and mining.
  • Supported by:
    National Natural Science Foundation of China(61976247).

Abstract: Time series anomaly detection is a challenging task due to complex temporal dependencies and limited labeled anomaly data.Previous methods have predominantly focused on modeling in the time domain,overlooking valuable information contained in the frequency domain,resulting in a certain degree of performance bottleneck.Taking this as a breakthrough,this paper proposes a Time-Frequency Attention Based Model for Time Series Anomaly Detection-TFA-TSAD,which firstly innovatively performs progressive decomposition of the input data to explore the anomalies of the data in different modes,and then utilizes the well-designed time-frequency domain modeling module to efficiently extract the time-domain information and frequency-domain information of the origin data respectively,by taking the attention mechanism to improve the performance of anomaly detection.Finally,based on the traditional error loss,a loss function incorporating average MSE is utilized to further improve model performance.Extensive experimental results on multiple datasets demonstrate that the proposed model outperforms 13 other benchmark models with a significant performance.

Key words: Time series anomaly detection, Time domain, Frequency domain, Attention mechanism, Decomposition

CLC Number: 

  • TP391.1
[1]LAI K H,ZHA D,XU J,et al.Revisiting time series outlier detection:Definitions and benchmarks[C]//Thirty-fifth Confe-rence on Neural Information Processing Systems Datasets and Benchmarks Track.2021.
[2]LI C,LAMMIE C,DONG X,et al.Seizure detection and prediction by parallel memristive convolutional neural networks[J].IEEE Transactions on Biomedical Circuits and Systems,2022,16(4):609-625.
[3]GOLMOHAMMADI K,ZAIANE O R.Time series contextual anomaly detection for detecting market manipulation in stock market[C]//2015 IEEE International Conference on Data Science and Advanced Analytics(DSAA).IEEE,2015:1-10.
[4]LIU F T,TING K M,ZHOU Z H.Isolation forest[C]//2008 Eighth IEEE International Conference on Data Mining.IEEE,2008:413-422.
[5]VAPNIK V,CHERVONENKIS A Y.A class of algorithms for pattern recognition learning[J].Avtomatikai Telemekhanika,1964,25(6):937-945.
[6]MACQUEEN J.Some methods for classification and analysis of multivariate observations[C]//Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability.1967:281-297.
[7]OEHMCKE S,ZIELINSKI O,KRAMER O.Event detection in marine time series data[C]//KI 2015:Advances in Artificial Intelligence:38th Annual German Conference on AI.Springer,2015:279-286.
[8]FU S,GAO X,ZHAI F,et al.A time series anomaly detection method based on series-parallel transformers with spatial and temporal association discrepancies[J].Information Sciences,2024,657:119978.
[9]YANG Y,ZHANG C,ZHOU T,et al.Dcdetector:Dual atten-tion contrastive representation learning for time series anomaly detection[C]//Proceedings of the 29th ACM SIGKDD Confe-rence on Knowledge Discovery and Data Mining.2023:3033-3045.
[10]GRÖCHENIG K.Uncertainty principles for time-frequency representations[M]//Advances in Gabor Analysis.2003:11-30.
[11]SHEN L,LI Z,KWOK J.Timeseries anomaly detection using temporal hierarchical one-class network[J].Advances in Neural Information Processing Systems,2020,33:13016-13026.
[12]HUNDMAN K,CONSTANTINOU V,LAPORTE C,et al.De-tecting spacecraft anomalies using lstms and nonparametric dynamic thresholding[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.387-395.
[13]REN H,XU B,WANG Y,et al.Time-series anomaly detection service at microsoft[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.3009-3017.
[14]AHMAD S,LAVIN A,PURDY S,et al.Unsupervised real-time anomaly detection for streaming data[J].Neurocomputing,2017,262:134-147.
[15]KINGMA D P.Auto-encoding variational bayes[J].arXiv:1312.6114,2013.
[16]ZONG B,SONG Q,MIN M R,et al.Deep autoencoding gaussian mixture model for unsupervised anomaly detection[C]//International Conference on Learning Representations.2018.
[17]WANG Z,FENG J,FU Q,et al.Quality control of online monitoring data of air pollutants using artificial neural networks[J].Air Quality,Atmosphere & Health,2019,12:1189-1196.
[18]ZHANG C,ZHOU T,WEN Q,et al.TFAD:A decomposition time series anomaly detection architecture with time-frequency analysis[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management.2022:2497-2507.
[19]VASWANI A.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.2017:6000-6010.
[20]XU J.Anomaly transformer:Time series anomaly detection with association discrepancy[J].arXiv:2110.02642,2021.
[21]NAM Y,YOON S,SHIN Y,et al.Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection[C]//Proceedings of the ACM on Web Conference.2024:4204-4215.
[22]TULI S,CASALE G,JENNINGS N R.Tranad:Deep trans-former networks for anomaly detection in multivariate time series data[J].arXiv:220107284,2022.
[23]JEONG Y,YANG E,RYU J H,et al.Anomalybert:Self-supervised transformer for time series anomaly detection using data degradation scheme[J].arXiv:2305.04468,2023.
[24]YI K,ZHANG Q,FAN W,et al.Frequency-domain MLPs are more effective learners in time series forecasting[J].arXiv:2311.06184,2023.
[25]WU H,HU T,LIU Y,et al.Timesnet:Temporal 2d-variationmodeling for general time series analysis[J].arXiv:2210.02186,2022.
[26]ZHOU T,MA Z,WEN Q,et al.Fedformer:Frequency enhanced decomposed transformer for long-term series forecasting[C]//International Conference on Machine Learning.PMLR,2022:27268-27286.
[27]WOO G,LIU C,SAHOO D,et al.Cost:Contrastive learning of disentangled seasonal-trend representations for time series forecasting[J].arXiv:2202.01575,2022.
[28]LEI T,GONG C,CHEN G,et al.A novel unsupervised framework for time series data anomaly detection via spectrum decomposition[J].Knowledge-Based Systems,2023,280:111002.
[29]WU H,XU J,WANG J,et al.Autoformer:Decomposition transformers with auto-correlation for long-term series forecasting[J].arXiv:2106.13008,2021.
[30]WANG H,PENG J,HUANG F,et al.Micn:Multi-scale local and global context modeling for long-term series forecasting[C]//The Eeleventh International Conference on Learning Representations.2023.
[31]LUO Y,LYU Z,HUANG X.TFDNet:Time-Frequency En-hanced Decomposed Network for Long-term Time Series Forecasting[J].arXiv:230813386,2023.
[32]WANG C,ZHUANG Z,QI Q,et al.Drift doesn′t matter:dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection[C]//Proceedings of the 37th International Conference on Neural Information Processing Systems.2024:10785-10774.
[33]SHIN Y,LEE S,TARIQ S,et al.Itad:integrative tensor-based anomaly detection system for reducing false positives of satellite systems[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management.2020:2733-27340.
[34]TARIQ S,LEE S,SHIN Y,et al.Detecting anomalies in space using multivariate convolutional LSTM with mixtures of probabilistic PCA[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:2123-21233.
[35]LI Z,ZHAO Y,HAN J,et al.Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.2021:3220-3230.
[36]SU Y,ZHAO Y,NIU C,et al.Robust anomaly detection for multivariate time series through stochastic recurrent neural network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:2828-2837.
[37]PARK D,HOSHI Y,KEMP C C.A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder[J].IEEE Robotics and Automation Letters,2018,3(3):1544-1551.
[38]FANG Y,XIE J,ZHAO Y,et al.Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection[C]//2024 IEEE 40th International Conference on Data Engineering(ICDE).IEEE,2024:1228-1241.
[1] ZHUO Tienong, YING Di, ZHAO Hui. Research on Student Classroom Concentration Integrating Cross-modal Attention and Role
Interaction
[J]. Computer Science, 2026, 53(2): 67-77.
[2] HAN Lei, SHANG Haoyu, QIAN Xiaoyan, GU Yan, LIU Qingsong, WANG Chuang. Constrained Multi-loss Video Anomaly Detection with Dual-branch Feature Fusion [J]. Computer Science, 2026, 53(2): 236-244.
[3] GUO Xingxing, XIAO Yannan, WEN Peizhi, XU Zhi, HUANG Wenming. Attention-based Audio-driven Digital Face Video Generation Method [J]. Computer Science, 2026, 53(2): 245-252.
[4] JI Sai, QIAO Liwei, SUN Yajie. Semantic-guided Hybrid Cross-feature Fusion Method for Infrared and Visible Light Images [J]. Computer Science, 2026, 53(2): 253-263.
[5] CHANG Xuanwei, DUAN Liguo, CHEN Jiahao, CUI Juanjuan, LI Aiping. Method for Span-level Sentiment Triplet Extraction by Deeply Integrating Syntactic and Semantic
Features
[J]. Computer Science, 2026, 53(2): 322-330.
[6] ZHANG Jing, PAN Jinghao, JIANG Wenchao. Background Structure-aware Few-shot Knowledge Graph Completion [J]. Computer Science, 2026, 53(2): 331-341.
[7] LYU Jinggang, GAO Shuo, LI Yuzhi, ZHOU Jin. Facial Expression Recognition with Channel Attention Guided Global-Local Semantic Cooperation [J]. Computer Science, 2026, 53(1): 195-205.
[8] FAN Jiabin, WANG Baohui, CHEN Jixuan. Method for Symbol Detection in Substation Layout Diagrams Based on Text-Image MultimodalFusion [J]. Computer Science, 2026, 53(1): 206-215.
[9] WANG Haoyan, LI Chongshou, LI Tianrui. Reinforcement Learning Method for Solving Flexible Job Shop Scheduling Problem Based onDouble Layer Attention Network [J]. Computer Science, 2026, 53(1): 231-240.
[10] CHEN Qian, CHENG Kaixuan, GUO Xin, ZHANG Xiaoxia, WANG Suge, LI Yanhong. Bidirectional Prompt-Tuning for Event Argument Extraction with Topic and Entity Embeddings [J]. Computer Science, 2026, 53(1): 278-284.
[11] WANG Cheng, JIN Cheng. KAN-based Unsupervised Multivariate Time Series Anomaly Detection Network [J]. Computer Science, 2026, 53(1): 89-96.
[12] LI Ang, ZHANG Jieyuan, LIU Xunyun. Camouflaged Object Detection for Aerial Images Based on Bidirectional Cross-attentionCross-domain Fusion [J]. Computer Science, 2026, 53(1): 173-179.
[13] PENG Jiao, HE Yue, SHANG Xiaoran, HU Saier, ZHANG Bo, CHANG Yongjuan, OU Zhonghong, LU Yanyan, JIANG dan, LIU Yaduo. Text-Dynamic Image Cross-modal Retrieval Algorithm Based on Progressive Prototype Matching [J]. Computer Science, 2025, 52(9): 276-281.
[14] GAO Long, LI Yang, WANG Suge. Sentiment Classification Method Based on Stepwise Cooperative Fusion Representation [J]. Computer Science, 2025, 52(9): 313-319.
[15] DAI Xiangguang, HE Chenglong, GUAN Mingyu, ZHANG Wei, ZHOU Yang, LIU Jianfeng, LYU Qingguo. State-decomposition Distributed Dual Averaging Algorithm for Privacy Online ConstrainedOptimization over Directed Networks [J]. Computer Science, 2025, 52(8): 411-420.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!