通过频域信息提升时间序列异常检测效果
F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain Information
- 结合频域分析与LSTM，利用滑动窗口和FFT构建频率矩阵
- 在Yahoo和Numenta数据集上优于现有方法，检测更精准
- 适合物联网和网络监控等需高效异常识别的场景
随着社会的发展，时间序列异常检测在网络安全和物联网服务中扮演关键角色。然而，多数现有方法仅在时域直接分析时间序列，难以识别某些隐含异常。本文从频域角度出发，提出一种新方法F-SE-LSTM，通过两个滑动窗口与快速傅里叶变换（FFT）构建频率矩阵；同时结合挤压激励网络（SENet）与长短期记忆网络（LSTM），提取周期内与周期间的频域特征。在Yahoo Webscope S5与Numenta Anomaly Benchmark等多个数据集上的对比实验表明，F-SE-LSTM构建的频率矩阵具有更强的区分能力，显著优于传统时域与频域数据。此外，该方法在异常检测性能与执行效率方面均超越现有先进深度学习方法。
原文摘要 · Abstract (English)
With the development of society, time series anomaly detection plays an important role in network and IoT services. However, most existing anomaly detection methods directly analyze time series in the time domain and cannot distinguish some relatively hidden anomaly sequences. We attempt to analyze the impact of frequency on time series from a frequency domain perspective, thus proposing a new time series anomaly detection method called F-SE-LSTM. This method utilizes two sliding windows and fast Fourier transform (FFT) to construct a frequency matrix. Simultaneously, Squeeze-and-Excitation Networks (SENet) and Long Short-Term Memory (LSTM) are employed to extract frequency-related features within and between periods. Through comparative experiments on multiple datasets such as Yahoo Webscope S5 and Numenta Anomaly Benchmark, the results demonstrate that the frequency matrix constructed by F-SE-LSTM exhibits better discriminative ability than ordinary time domain and frequency domain data. Furthermore, F-SE-LSTM outperforms existing state-of-the-art deep learning anomaly detection methods in terms of anomaly detection capability and execution efficiency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。