AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data

Abstract

Detecting anomalous subsequences in time series data is one of the key tasks in time series analytics, having applications in environmental monitoring, preventive healthcare, predictive maintenance, and many further areas. Data scientists have developed various anomaly detection algorithms with individual strengths, such as the ability to detect repeating anomalies, anomalies in non-periodic time series, or anomalies with varying lengths. For a given dataset and task, the best algorithm with a suitable parameterization and, in some cases, sufficient training data, usually solves the anomaly detection problem well. However, given the high number of existing algorithms, their numerous parameters, and a pervasive lack of training data and domain knowledge, effective anomaly detection is still a complex task that heavily relies on manual experimentation. We propose the unsupervised AutoTSAD system, which parameterizes, executes, and ensembles various highly effective anomaly detection algorithms. The ensembling system automatically presents an aggregated anomaly scoring for an arbitrary time series without a need for training data or parameter expertise. Our experiments show that AutoTSAD offers an anomaly detection accuracy comparable to the best manually optimized anomaly detection algorithms, and can significantly outperform existing method selection and ensembling approaches for time series anomaly detection.