Computer Science ›› 2026, Vol. 53 ›› Issue (1): 89-96.doi: 10.11896/jsjkx.241200190

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

KAN-based Unsupervised Multivariate Time Series Anomaly Detection Network

WANG Cheng, JIN Cheng   

  1. School of Computer Science, Fudan University, Shanghai 200438, China
  • Received:2024-12-26 Revised:2025-03-12 Online:2026-01-15 Published:2026-01-08
  • About author:WANG Cheng,born in 1985,Ph.D candidate.His main research interest is time series anomaly detection.
    JIN Cheng,born in 1978,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.08763S).His main research interests include computer vision and multimedia information retrieval.
  • Supported by:
    National Natural Science Foundation of China(62472097).

Abstract: Time series data is widely present in fields such as finance,healthcare,industry,and transportation.Time Series Ano-maly Detection(TSAD) is crucial for ensuring system stability and safety.Most current time series anomaly detection methods are unsupervised due to the difficulty in collecting anomaly samples.However,these methods commonly face the problem of over-generalization,where the model can not only reconstruct normal samples,but also effectively reconstruct anomaly samples,leading to poor anomaly detection performance.Therefore,this paper proposes a time series anomaly detection method based on Kolmo-gorov-Arnold representation theory,called TS-KAN.TS-KAN leverages its parameter efficiency and local plasticity to better fit normal samples and alleviate the overgeneralization problem.Additionally,this paper introduces a local feature enhancement layer,namely Local-KAN,to enhance the representation of temporal features and improve contextual anomaly detection capability.Experiments on five mainstream time series anomaly detection datasets demonstrate that TS-KAN significantly outperforms existing methods in anomaly detection capability.

Key words: Time series anomaly detection, KAN, Transformer, Memory module, Deep learning

CLC Number: 

  • TP391
[1]ZONG B,SONG Q,MIN MARTIN R Q,et al.Deep autoenco-ding gaussian mixture model for unsupervised anomaly detection[C]//International Conference on Learning Representations.2018.
[2]YAIRI T,TAKEISHI N,ODA T,et al.A data-driven healthmonitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction[J].IEEE Transactions on Aerospace and Electronic Systems,2017,53(3):1384-1401.
[3]ZHOU Q H,HE S B,LIU H Y,et al.Label-free multivariate time series anomaly detection[J].IEEE Transactions on Know-ledge and Data Engineering,2024,36(7):3166-3179.
[4]FREHNER R B,WU K S,SIM A,et al.Detecting Anomalies in Time Series Using Kernel Density Approaches[J].IEEE Access,2024,12:33420-33439.
[5]SHEN L F,LI Z C,KWOK J.Timeseries anomaly detectionusing temporal hierarchical one-class network[J].Advances in Neural Information Processing Systems,2020,33:13016-13026.
[6]SHIN Y J,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.2020:2733-2740.
[7]DONG C,TAO J F,CHAO Q,et al.Subsequence time series clustering-based unsupervised approach for anomaly detection of axial piston pumps[J].IEEE Transactions on Instrumentation and Measurement,2023,72:1-12.
[8]HUNDMAN K,CONSTANTINOU V,LAPORTE C,et al.Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.2018:387-395.
[9]TARIQ S,LEE S,SHIN Y J,et al.Detecting anomaliesin space using multivariate convolutional LSTM with mixtures of probabilistic PCA[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:2123-2133.
[10]PARK D,HOSHI Y,KEMP CHARLES C.A multimodal ano-maly detector for robot-assisted feeding using an lstm-based variational autoencoder[J].IEEE Robotics and Automation Letters,2018,3(3):1544-1551.
[11]SU Y,ZHAO Y J,NIU C H,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.
[12]LI Z H,ZHAO Y J,HAN J Q,et al.Multivariate time seriesanomaly 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.
[13]XU J,WU H,WANG J,et al.Anomaly Transformer:Time Series Anomaly Detection with Association Discrepancy[C]//International Conference on Learning Representations.2022.
[14]SUN Y Y,CHEN Z D,FENG C,et al.UMTS-Mixer:Time Series Anomaly Detection Based on Temporal Correlation and Channel Correlation[J].Computer Systems and Applications,2024,33(1):127-133.
[15]YE L,HE Z.Multiscale time series anomaly detection incorporating wavelet decomposition[J].Journal of Computer Applications,2024,44(10):3300-3306.
[16]GONG D,LIU L Q,LE V,et al.Memorizing normality to detect anomaly:Memory-augmented deep autoencoder for unsupervised anomaly detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:1705-1714.
[17]PARK H J,NOH J,HAM B.Learning memory-guided normality for anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:14372-14381.
[18]SONG J,KIM K,OH J,et al.MEMTO:Memory-guided transformer for multivariate time series anomaly detection[J].arXiv:2312.02530,2023.
[19]LIU Z M,WANG Y X,VAIDYA S,et al.KAN:Kolmogorov-arnold networks[J].arXiv:2404.19756,2024.
[20]LIU Z M,MA P C,WANG Y X,et al.KAN 2.0:Kolmogorov-Arnold Networks Meet Science[J].arXiv:2408.10205,2024.
[21]SIDHARTH S S.Chebyshev polynomial-based kolmogorov-arnold networks:An efficient architecture for nonlinear function approximation[J].arXiv:2405.07200,2024.
[22]AGHAEI A A.fKAN:Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions[J].arXiv:2406.07456,2024.
[23]BOZORGASL Z,CHEN H.Wav-KAN:Wavelet kolmogorov-arnold networks[J].arXiv:2405.12832,2024.
[24]LI C X,LIU X Y,LI W Y,et al.U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation[J].arXiv:2406.02918,2024.
[25]VACA-RUBIO C J,BLANCO L,PEREIRA R,et al.Kolmogorov-arnold networks(kans) for time series analysis[J].arXiv:2405.08790,2024.
[26]GENET R,INZIRILLO H.TKAN:Temporal Kolmogorov-Arnold Networks[J].arXiv:2405.07344,2024.
[27]SONODA S,MURATA N.Neural network with unboundedactivation functions is universal approximator[J].Applied and Computational Harmonic Analysis,2017,43(2):233--268.
[28]LAI M J,SHEN Z M.The kolmogorov superposition theoremcan break the curse of dimensionality when approximating high dimensional functions[J].arXiv:2112.09963,2021.
[29]ELFWING S,UCHIBE E,DOYA K.Sigmoid-weighted linearunits for neural network function approximation in reinforcement learning[J].Neural networks,2018,107:3-11.
[30]SUBAKAN C,RAVANELLI M,CORNELL S,et al.Attention is all you need in speech separation[C]//2021 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).2021:21-25.
[31]SARFRAZ M S,CHEN M Y,LAYER L,et al.Position:Quo Vadis,Unsupervised Time Series Anomaly Detection?[C]//Proceedings of the 41st International Conference on Machine Learning.2024:43461-43476.
[32]SU Y,ZHAO Y J,NIU C H,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.
[33]HUNDMAN K,CONSTANTINOU V,LAPORTE C,et al.Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.2018:387-395.
[34]LI D,CHEN D C,JIN B H,et al.MAD-GAN:Multivariateanomaly detection for time series data with generative adversarial networks[C]//International Conference on Artificial Neural Networks.2019:703-716.
[35]ABDULAAL A,LIU Z H,LANCEWICKI T.Practical approach to asynchronous multivariate time series anomaly detection and localization[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.2021:2485-2494.
[36]WU R,KEOGH E J.Current time series anomaly detectionbenchmarks are flawed and are creating the illusion of progress[J].IEEE Transactions on Knowledge and Data Engineering,2021,35(3):2421-2429.
[1] CHEN Yuansheng, CHEN Shunjue, MO Xuan, WU Weigang, LI Jialun. Deep Learning Training Time Prediction Algorithm Integrating Multi-dimensional Operator Features [J]. Computer Science, 2026, 53(5): 129-136.
[2] LI Tengjia, MA Chun’ai. Multi-scale Transformer Oil Price Prediction Framework with AEMD and Trend Cross-attention [J]. Computer Science, 2026, 53(5): 157-163.
[3] YANG Hongju, ZHANG Ziyang, LI Yao. Frequency Driven Multi-scale Image Super-resolution Method [J]. Computer Science, 2026, 53(5): 218-227.
[4] GUO Jingchen, YANG Kuiwu, DING Mengdi, WEI Jianghong. Survey of Adversarial Sample Attacks for Vision Transformer [J]. Computer Science, 2026, 53(5): 404-418.
[5] GAO Tai, REN Yanzhang, WANG Huiqing, LI Ying, WANG Bin. KGMamba:Gene Regulatory Network Prediction Model Based on Kolmogorov-Arnold Network Optimizing Graph Convolutional Network and Mamba [J]. Computer Science, 2026, 53(4): 101-111.
[6] ZHANG Xueqin, WANG Zhineng, LI Jinsheng, LU Yisong, LUO Fei. Key Node Identification in Temporal Social Networks Based on Deep Learning and Multi-feature Fusion [J]. Computer Science, 2026, 53(4): 143-154.
[7] GU Bokai, LIU Dun, SUN Yang. STWD-DLFRD:Multi-granularity Fake Review Detection via Sequential Three-way Decisions and Deep Learning [J]. Computer Science, 2026, 53(4): 188-196.
[8] XIN Yichen, LI Shichong, CHEN Bin, CHENG Zhangtao, LI Ye, ZHOU Fan. Enhancing Temporal Knowledge Graph Reasoning Method with Graph Information Bottleneck and Transformer [J]. Computer Science, 2026, 53(4): 393-405.
[9] ZHENG Cheng, BAN Qingqing. Knowledge-assisted and Reinforced Syntax-driven for Aspect-based Sentiment Analysis [J]. Computer Science, 2026, 53(4): 406-414.
[10] YIN Chuang, LIU Jianyi, ZHANG Ru. Cross-modal Fusion Few-sample Ransomware Classifier:Multimodal Encoding Based on Pre-trained Models [J]. Computer Science, 2026, 53(4): 435-444.
[11] FU Yukai, LI Qingzhen, DONG Zhixue, SHI Dongli, ZHAO Peng. Pedestrian Re-identification Methods Based on Limited Target Data and Deep Learning [J]. Computer Science, 2026, 53(3): 287-294.
[12] YU Ding, LI Zhangwei. Prediction Method of RNA Secondary Structure Based on Transformer Architecture [J]. Computer Science, 2026, 53(3): 375-382.
[13] DU Jiantong, GUAN Zeli, XUE Zhe. Multi-task Learning-based Ophthalmic Video Feature Fusion and Multi-dimensional Profiling [J]. Computer Science, 2026, 53(3): 383-391.
[14] SU Ruitao, REN Jiongjiong, CHEN Shaozhen. Deep Learning-based Neural Differential Distinguishers for GIFT-128 and ASCON [J]. Computer Science, 2026, 53(3): 453-458.
[15] CHEN Han, XU Zefeng, JIANG Jiu, FAN Fan, ZHANG Junjian, HE Chu, WANG Wenwei. Large Language Model and Deep Network Based Cognitive Assessment Automatic Diagnosis [J]. Computer Science, 2026, 53(3): 41-51.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!