KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks

2024-11-01

arXiv.org

AI Summary

To address the high false-alarm rate in time-series anomaly detection (TSAD) for cloud services and web systems—caused by overfitting to minor fluctuations—this paper proposes a robust smoothing-based modeling framework. It replaces B-splines with truncated Fourier expansions to construct more stable local normal-pattern representations, designs a lightweight global-aware learning mechanism to enable global-local co-optimization, and builds upon the Kolmogorov–Arnold network (KAN) architecture with a parameter-efficient training strategy. The resulting model employs fewer than 1,000 parameters and achieves a 50% inference speedup over the baseline KAN. Evaluated on four standard benchmarks, it attains an average 15% improvement in detection accuracy (peaking at 27%) and demonstrates significantly enhanced robustness against noise and practical applicability.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web

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 function ensures that the fitted time series remains resilient against local disturbances. However, a direct KAN implementation proves susceptible to these disturbances due to the inherently localized characteristics of B-spline functions. We thus propose KAN-AD, replacing B-splines with truncated Fourier expansions and introducing a novel lightweight learning mechanism that emphasizes global patterns while staying robust to local disturbances. On four popular TSAD benchmarks, KAN-AD achieves an average 15% improvement in detection accuracy (with peaks exceeding 27%) over state-of-the-art baselines. Remarkably, it requires fewer than 1,000 trainable parameters, resulting in a 50% faster inference speed compared to the original KAN, demonstrating the approach's efficiency and practical viability.

Problem

• Detect anomalies in time series data effectively
• Overcome overfitting by focusing on smooth normal patterns
• Improve accuracy and speed with lightweight learning

Innovation

• Replaces B-splines with truncated Fourier expansions
• Introduces lightweight learning for global patterns
• Ensures robustness against local disturbances