The Good, the Bad, and the Average: Benchmarking of Reconstruction Based Multivariate Time Series Anomaly Detection
Abstract
Reconstruction-based algorithms offer state-of-the-art performance in multivariate time series anomaly detection. But as always: there is no single best algorithm. To find the optimal solution, one has to compare different methods and tune their hyperparameters. This paper introduces a lightweight modular benchmarking framework for data scientists and researchers in the field. The framework can be easily set up and automatically create a visual summary of the relevant performance indicators and automatically selected examples to give insight into the behavior of the model and aid during the development.
Cite
Text
Baudzus et al. "The Good, the Bad, and the Average: Benchmarking of Reconstruction Based Multivariate Time Series Anomaly Detection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. doi:10.1007/978-3-031-43430-3_30Markdown
[Baudzus et al. "The Good, the Bad, and the Average: Benchmarking of Reconstruction Based Multivariate Time Series Anomaly Detection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023.](https://mlanthology.org/ecmlpkdd/2023/baudzus2023ecmlpkdd-good/) doi:10.1007/978-3-031-43430-3_30BibTeX
@inproceedings{baudzus2023ecmlpkdd-good,
title = {{The Good, the Bad, and the Average: Benchmarking of Reconstruction Based Multivariate Time Series Anomaly Detection}},
author = {Baudzus, Arn and Li, Bin and Jadid, Adnane and Müller, Emmanuel},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2023},
pages = {356-360},
doi = {10.1007/978-3-031-43430-3_30},
url = {https://mlanthology.org/ecmlpkdd/2023/baudzus2023ecmlpkdd-good/}
}