Time series anomaly detection based on shapelet learning

Abstract

We consider the problem of learning to detect anomalous time series from an unlabeled data set, possibly contaminated with anomalies in the training data. This scenario is important for applications in medicine, economics, or industrial quality control, in which labeling is difficult and requires expensive expert knowledge, and anomalous data is difficult to obtain. This article presents a novel method for unsupervised anomaly detection based on the shapelet transformation for time series. Our approach learns representative features that describe the shape of time series stemming from the normal class, and simultaneously learns to accurately detect anomalous time series. An objective function is proposed that encourages learning of a feature representation in which the normal time series lie within a compact hypersphere of the feature space, whereas anomalous observations will lie outside of a decision boundary. This objective is optimized by a block-coordinate descent procedure. Our method can efficiently detect anomalous time series in unseen test data without retraining the model by reusing the learned feature representation. We demonstrate on multiple benchmark data sets that our approach reliably detects anomalous time series, and is more robust than competing methods when the training instances contain anomalous time series.