Summary of F-se-lstm: a Time Series Anomaly Detection Method with Frequency Domain Information, by Yi-xiang Lu et al.
F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain Information
by Yi-Xiang Lu, Xiao-Bo Jin, Jian Chen, Dong-Jie Liu, Guang-Gang Geng
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The paper proposes a new time series anomaly detection method called F-SE-LSTM, which analyzes the impact of frequency on time series from a frequency domain perspective. The method utilizes two sliding windows, fast Fourier transform (FFT), Squeeze-and-Excitation Networks (SENet), and Long Short-Term Memory (LSTM) to extract frequency-related features within and between periods. Experimental results show that F-SE-LSTM outperforms existing state-of-the-art deep learning anomaly detection methods in terms of anomaly detection capability and execution efficiency on multiple datasets, including Yahoo Webscope S5 and Numenta Anomaly Benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to find unusual patterns in time series data. This method looks at the frequency, or rhythm, of the data instead of just its timing. It uses special algorithms like SENet and LSTM to identify features that are important for detecting anomalies. The results show that this approach is better than others at finding these unusual patterns and it’s also more efficient. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning » Lstm » Time series
