Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly Detection

Jose Manuel Navarro, Alexis Huet, Dario Rossi
Proceedings of the Second International Conference on Automated Machine Learning, PMLR 224:24/1-19, 2023.

Abstract

Unsupervised model recommendation for anomaly detection is a recent discipline for which there is no existing work that focuses on multivariate time series data. This paper studies that problem under real-world restrictions, most notably: (i) a limited time to issue a recommendation, which renders existing methods based around the testing of a large pool of models unusable; (ii) the need for generalization to previously unseen data sources, which is seldom factored in the experimental evaluation. We turn to meta-learning and propose Hydra, the first meta-recommender for anomaly detection in literature that we especially analyze in the context of multivariate times series. We conduct our experiments using 94 public datasets from 4 different data sources. Our ablation study testifies that our meta-recommender achieves a higher performance than the current state of the art, including in difficult scenarios in which data similarity is minimal: our proposal is able to recommend a model in the top 10% (13%) of the algorithmic pool for known (unseen) sources of data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v224-navarro23a, title = {Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly Detection }, author = {Navarro, Jose Manuel and Huet, Alexis and Rossi, Dario}, booktitle = {Proceedings of the Second International Conference on Automated Machine Learning}, pages = {24/1--19}, year = {2023}, editor = {Faust, Aleksandra and Garnett, Roman and White, Colin and Hutter, Frank and Gardner, Jacob R.}, volume = {224}, series = {Proceedings of Machine Learning Research}, month = {12--15 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v224/navarro23a/navarro23a.pdf}, url = {https://proceedings.mlr.press/v224/navarro23a.html}, abstract = {Unsupervised model recommendation for anomaly detection is a recent discipline for which there is no existing work that focuses on multivariate time series data. This paper studies that problem under real-world restrictions, most notably: (i) a limited time to issue a recommendation, which renders existing methods based around the testing of a large pool of models unusable; (ii) the need for generalization to previously unseen data sources, which is seldom factored in the experimental evaluation. We turn to meta-learning and propose Hydra, the first meta-recommender for anomaly detection in literature that we especially analyze in the context of multivariate times series. We conduct our experiments using 94 public datasets from 4 different data sources. Our ablation study testifies that our meta-recommender achieves a higher performance than the current state of the art, including in difficult scenarios in which data similarity is minimal: our proposal is able to recommend a model in the top 10% (13%) of the algorithmic pool for known (unseen) sources of data.} }
Endnote
%0 Conference Paper %T Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly Detection %A Jose Manuel Navarro %A Alexis Huet %A Dario Rossi %B Proceedings of the Second International Conference on Automated Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Aleksandra Faust %E Roman Garnett %E Colin White %E Frank Hutter %E Jacob R. Gardner %F pmlr-v224-navarro23a %I PMLR %P 24/1--19 %U https://proceedings.mlr.press/v224/navarro23a.html %V 224 %X Unsupervised model recommendation for anomaly detection is a recent discipline for which there is no existing work that focuses on multivariate time series data. This paper studies that problem under real-world restrictions, most notably: (i) a limited time to issue a recommendation, which renders existing methods based around the testing of a large pool of models unusable; (ii) the need for generalization to previously unseen data sources, which is seldom factored in the experimental evaluation. We turn to meta-learning and propose Hydra, the first meta-recommender for anomaly detection in literature that we especially analyze in the context of multivariate times series. We conduct our experiments using 94 public datasets from 4 different data sources. Our ablation study testifies that our meta-recommender achieves a higher performance than the current state of the art, including in difficult scenarios in which data similarity is minimal: our proposal is able to recommend a model in the top 10% (13%) of the algorithmic pool for known (unseen) sources of data.
APA
Navarro, J.M., Huet, A. & Rossi, D.. (2023). Meta-Learning for Fast Model Recommendation in Unsupervised Multivariate Time Series Anomaly Detection . Proceedings of the Second International Conference on Automated Machine Learning, in Proceedings of Machine Learning Research 224:24/1-19 Available from https://proceedings.mlr.press/v224/navarro23a.html.

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