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

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.