Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
This work addresses the problem of method selection for anomaly detection in time series, relevant to fields like system monitoring and cybersecurity, but it is incremental as it focuses on empirical comparison without introducing new techniques.
The study compared six unsupervised anomaly detection methods on time series data using the UCR anomaly archive benchmark, finding that classical machine learning methods generally outperformed deep learning methods across various anomaly types.
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.