Res2coder: A two-stage residual autoencoder for unsupervised time series anomaly detection

Detecting anomalies in multivariate time series data is crucial for various industries. However, the increasing volume and dimensionality of data have driven up the cost of data labeling, making supervised or semi-supervised methods less effective. Unsupervised learning methods, which do not require labeled training data, have steadily gained importance due to their ability to reduce data processing costs. This paper proposes an unsupervised multivariate time series anomaly detection method - Res2coder. It decomposes time series data into trend and residual components for separate reconstruction, and incorporates frequency-domain analysis. Res2coder uses an MLP-based autoencoder module to reconstruct each component. Additionally, two error feedback mechanisms are designed in the reconstruction module to enable the model to more accurately capture the features and changing patterns of the data. This approach improves detection accuracy and reduces model training costs without relying on complex networks like convolutions or attention mechanisms. We compare Res2coder with several baseline models (e.g., TranAD and ATF-UAD) on six datasets (e.g., SWaT, WADI, and SMD). It achieves higher scores in six evaluation metrics-precision, recall, F1, ROC/AUC, Composite F-score (Fc1), and Real Under Point Adjustment %K Curve (PA%K). Moreover, Res2coder reduces training time by 50% to 90%.