Sunday 02 February 2025
The quest for anomaly detection in time series data has long been a thorny problem for researchers and practitioners alike. The complexity of identifying unusual patterns within vast amounts of temporal data can be overwhelming, making it a challenge to develop effective methods for detecting anomalies.
Enter a new approach that leverages the frequency domain to uncover hidden patterns in time series data. By employing a sliding window technique and Fourier Transform (FFT), this method constructs a frequency matrix that captures the essence of the original time series data. This novel approach is then coupled with a Squeeze-and-Excitation Network (SENet) and Long Short-Term Memory (LSTM) to extract frequency-related information within and between time periods.
The proposed F-SE-LSTM model outperforms existing methods in terms of accuracy, recall, precision, and F1 score on multiple real-world datasets. This is due in part to the ability of the SENet module to selectively emphasize or suppress different frequency components, allowing the model to better adapt to the underlying structure of the data.
A key advantage of this approach lies in its ability to handle large-scale time series datasets with ease. The use of a sliding window technique ensures that the model can process data streams of varying lengths without sacrificing performance, making it particularly well-suited for real-world applications where data streams are often unpredictable and irregular.
Furthermore, the proposed method is shown to be robust against various types of noise and anomalies, demonstrating its ability to generalize across different datasets and scenarios. This resilience is likely due to the combination of frequency-domain features and temporal information captured by the LSTM module.
While this approach may not revolutionize the field of anomaly detection overnight, it certainly offers a promising new direction for researchers and practitioners seeking to improve their methods. As the volume and complexity of time series data continues to grow, the need for effective anomaly detection techniques has never been more pressing. The proposed F-SE-LSTM model represents an important step forward in this ongoing quest.
In contrast to traditional approaches that rely solely on time-domain features or frequency-domain features, this method combines both domains to create a more comprehensive understanding of the underlying data structure. This synergy allows the model to better capture subtle patterns and anomalies that might otherwise go unnoticed.
The results speak for themselves: the proposed F-SE-LSTM model outperforms existing methods across multiple datasets and evaluation metrics, demonstrating its potential as a valuable tool in the toolkit of any serious practitioner or researcher working with time series data.
Cite this article: “Frequency-Domain Anomaly Detection with F-SE-LSTM Model”, The Science Archive, 2025.
Anomaly Detection, Time Series Data, Frequency Domain, Fourier Transform, Sliding Window Technique, Squeeze-And-Excitation Network, Long Short-Term Memory, Lstm, Senet, F1 Score
