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.

翻译：随着社会的发展，时间序列异常检测在网络和物联网服务中扮演着重要角色。然而，现有的大多数异常检测方法直接在时域中分析时间序列，难以区分某些相对隐蔽的异常序列。我们尝试从频域角度分析频率对时间序列的影响，从而提出一种新的时间序列异常检测方法，称为F-SE-LSTM。该方法利用两个滑动窗口和快速傅里叶变换（FFT）构建频率矩阵。同时，采用压缩与激励网络（SENet）和长短期记忆网络（LSTM）来提取周期内和周期间与频率相关的特征。通过在Yahoo Webscope S5和Numenta Anomaly Benchmark等多个数据集上的对比实验，结果表明，F-SE-LSTM构建的频率矩阵比普通的时域和频域数据表现出更好的判别能力。此外，F-SE-LSTM在异常检测能力和执行效率方面均优于现有的先进深度学习异常检测方法。