KAN-based Unsupervised Multivariate Time Series Anomaly Detection Network

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.