Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?
This work addresses the gap in comparative studies for multivariate time series anomaly detection, encouraging the community to include diverse method categories in benchmarks, though it is incremental as it synthesizes existing approaches.
The study compared 16 conventional, machine learning, and deep neural network methods for anomaly detection in multivariate time series across five real-world datasets, finding that no single family of methods consistently outperforms the others.
Anomaly detection in time series is a complex task that has been widely studied. In recent years, the ability of unsupervised anomaly detection algorithms has received much attention. This trend has led researchers to compare only learning-based methods in their articles, abandoning some more conventional approaches. As a result, the community in this field has been encouraged to propose increasingly complex learning-based models mainly based on deep neural networks. To our knowledge, there are no comparative studies between conventional, machine learning-based and, deep neural network methods for the detection of anomalies in multivariate time series. In this work, we study the anomaly detection performance of sixteen conventional, machine learning-based and, deep neural network approaches on five real-world open datasets. By analyzing and comparing the performance of each of the sixteen methods, we show that no family of methods outperforms the others. Therefore, we encourage the community to reincorporate the three categories of methods in the anomaly detection in multivariate time series benchmarks.