Summary of Kan-ad: Time Series Anomaly Detection with Kolmogorov-arnold Networks, by Quan Zhou et al.
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
by Quan Zhou, Changhua Pei, Fei Sun, Jing Han, Zhengwei Gao, Dan Pei, Haiming Zhang, Gaogang Xie, Jianhui Li
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel deep learning-based method for time series anomaly detection (TSAD), specifically designed to address the issue of noise in existing methods. The proposed approach, called KAN-AD, leverages Fourier series to emphasize global temporal patterns, mitigating the influence of local peaks and drops that can affect TSAD performance. By transforming the traditional black-box learning approach into learning weights preceding univariate functions, KAN-AD achieves a 15% accuracy increase over current state-of-the-art methods while boosting inference speed by 55 times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series anomaly detection is important for cloud services and web systems because it can help prevent losses. Existing deep learning-based forecasting methods are popular but don’t always work well due to noise in the data. Noise is when there are big spikes or dips in a time series that aren’t really important. This noise can make it hard for machines to learn what’s normal, so they misidentify anomalies. The new method, KAN-AD, tries to solve this problem by breaking down complex time series into simpler patterns and focusing on the global patterns rather than local peaks and drops. |
Keywords
» Artificial intelligence » Anomaly detection » Boosting » Deep learning » Inference » Time series
