Anomaly Detection for Symbolic Time Series Representations of Reduced Dimensionality

The systematic collection of data has become an intrinsic process of all aspects in modern life. From industrial to healthcare machines and wearable sensors, an unprecedented amount of data is becoming available for mining and information retrieval. In particular, anomaly detection plays a key role in a wide range of applications, and has been studied extensively. However, many anomaly detection methods are unsuitable in practical scenarios, where streaming data of large volume arrive in nearly real-time at devices with limited resources. Dimensionality reduction has been excessively used to enable efficient processing for numerous high-level tasks. In this paper, we propose a computationally efficient, yet highly accurate, framework for anomaly detection of streaming data in lower-dimensional spaces, utilizing a modification of the symbolic aggregate approximation for dimensionality reduction and a statistical hypothesis testing based on the Kullback-Leibler divergence.