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.
Do deep neural networks contribute to multivariate time series anomaly detection?
Pattern Recognition, Vol. 132, December 2022, 108945
Type:
Journal
Date:
2022-07-26
Department:
Data Science
Eurecom Ref:
6863
Copyright:
© Elsevier. Personal use of this material is permitted. The definitive version of this paper was published in Pattern Recognition, Vol. 132, December 2022, 108945 and is available at : https://doi.org/10.1016/j.patcog.2022.108945
See also:
PERMALINK : https://www.eurecom.fr/publication/6863