AI RESEARCH
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
arXiv CS.LG
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ArXi:2411.00278v4 Announce Type: replace Time series anomaly detection (TSAD) underpins real-time monitoring in cloud services and web systems, allowing rapid identification of anomalies to prevent costly failures. Most TSAD methods driven by forecasting models tend to overfit by emphasizing minor fluctuations. Our analysis reveals that effective TSAD should focus on modeling "normal" behavior through smooth local patterns. To achieve this, we reformulate time series modeling as approximating the series with smooth univariate functions.