arXiv:2411.00278v4 Announce Type: replace Abstract: 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. The local smoothness of each univariate functi
The continuous evolution of AI models demands new architectural approaches to address specific challenges like 'overfitting' in time series anomaly detection, preventing costly failures in critical systems.
Improved anomaly detection enhances the reliability and efficiency of real-time monitoring in cloud services and web systems, directly impacting operational stability and cost management for technology infrastructure.
This research introduces a novel application of Kolmogorov-Arnold Networks (KANs) to improve the accuracy and robustness of time series anomaly detection by better modeling normal behavior.
- · Cloud service providers
- · Web system operators
- · AI/ML researchers and developers
- · Industries relying on critical real-time monitoring
- · Systems prone to subtle time-series anomalies
- · Less robust time series anomaly detection methods
More accurate anomaly detection leads to fewer system outages and improved service reliability for end-users.
Reduced operational costs for maintaining complex IT infrastructures due to fewer anomaly-related incidents.
Increased trust in automated monitoring systems, potentially accelerating the adoption of AI for mission-critical operations across diverse sectors.
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