An Experimental Evaluation of Anomaly Detection in Time Series

Anomaly detection in time series data has been studied for decades in both statistics and computer science. Various algorithms have been proposed for different scenarios, such as fraud detection, environmental monitoring, manufacturing, and healthcare. However, there is a lack of comparative evaluation of these state-of-the-art approaches, especially in the same test environment and with the same benchmark, making it difficult for users to select an appropriate method for real-world applications. In this paper, we present a taxonomy of anomaly detection methods based on the main features, i.e., data dimension, processing technique, and anomaly type and six inner classes. We perform systematic intra- and inter-class comparisons of seventeen state-of-the-art algorithms on real and synthetic datasets with a point metric commonly used in classification problems and a range metric specifically designed for subsequence anomalies in time series data. We analyze the properties of these algorithms and test them in terms of effectiveness, efficiency, and robustness to anomaly rates, data sizes, number of dimensions, anomaly patterns, and threshold settings. We also test their performance in different use cases. Finally, we provide a practical guide for detecting anomalies in time series and discussions.