Explainable Abnormal Time Series Subsequence Detection Using Random Convolutional Kernels

Abstract

To identify anomalous subsequences in time series, it is a common practice to convert them into a set of features prior to the use of an anomaly detector. Feature extraction can be accomplished by manually designing the features or by automatically learning them using a neural network. However, for the former, significant domain expertise is required to design features that are effective in accurately detecting anomalies, while in the latter, it might be complex to learn useful features when dealing with unsupervised or one-class classification problems such as anomaly detection, where there are no labels available to guide the feature extraction process. In this paper, we propose an alternative approach for feature extraction that overcomes the limitations of the two previously mentioned approaches. The proposed method involves calculating the similarities between subsequences and a set of randomly generated convolutional kernels and the use of the One-Class SVM algorithm. We tested our approach on voltage signals acquired during circular welding processes in hot water tank manufacturing, the results indicate that the approach achieves higher accuracy in detecting welding defects in comparison to commonly used methods. Furthermore, we introduce in this work an approach for explaining the anomalies detected by making use of the random convolutional kernels, which addresses an important gap in time series anomaly detection.