Learning Local Patterns of Time Series for Anomaly Detection

Abstract

The problem of anomaly detection in time series has recently received much attention, but in most practical applications, labels for normal and anomalous data are not available. Furthermore, reasons for anomalous results must often be determined. In this paper, we propose a new anomaly detection method based on the expectation–maximization algorithm, which learns the probabilistic behavior of local patterns inherent in time series in an unsupervised manner. The proposed method is simple yet enables anomaly detection with accuracy comparable with that of the conventional method. In addition, the representation of local patterns based on probabilistic models provides new insight that can be used to determine reasons for anomaly detection decisions.

Keywords

• time series
• anomaly detection
• subsequence
• visualization