Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series

TL;DR

This study compares six unsupervised anomaly detection methods in time series, revealing that classical machine learning methods often outperform deep learning approaches across various anomaly types.

Contribution

The paper provides a comprehensive comparison of classical and deep learning methods for time series anomaly detection using a recent benchmark dataset.

Findings

Classical methods generally outperform deep learning methods.

Performance varies depending on anomaly type and dataset characteristics.

Incorporating prior knowledge can influence detection effectiveness.

Abstract

Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare, and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given application, each method has its strengths in detecting certain types of anomalies. In this study, we compare six unsupervised anomaly detection methods of varying complexity to determine whether more complex methods generally perform better and if certain methods are better suited to certain types of anomalies. We evaluated the methods using the UCR anomaly archive, a recent benchmark dataset for anomaly detection. We analyzed the results on a dataset and anomaly type level after adjusting the necessary hyperparameters for each method. Additionally, we assessed the ability of each method to incorporate prior knowledge about anomalies and examined the differences between point-wise and sequence-wise features. Our experiments show that classical machine learning methods generally outperform deep learning methods across a range of anomaly types.