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

Published: 16 May 2023, Last Modified: 05 Sept 2023AutoML 2023 MainTrackReaders: Everyone
Keywords: anomaly detection, meta-learning, automatic model recommendation, multivariate time series
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.
Submission Checklist: Yes
Broader Impact Statement: Yes
Paper Availability And License: Yes
Code Of Conduct: Yes
Reviewers: Yes
CPU Hours: 0
GPU Hours: 0
TPU Hours: 0
TL;DR: Meta-learning for anomaly detection presenting a meta-recommender that adapts dynamically to data novelty
Code And Dataset Supplement: zip
16 Replies

Loading