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We consider the problem of anomaly detection in multiple co-evolving data streams. In this paper, we introduce FRAHST (Fast Rank-Adaptive row-Householder Subspace Tracking). It automatically learns the principal subspace from N numerical data streams and an anomaly is indicated by a change in the number of latent variables. Our technique provides state-of-the-art estimates for the subspace basis and has a true dominant complexity of only 5N r operations while satisfying all desirable streamingdoi:10.1145/1774088.1774434 dblp:conf/sac/TeixeiraM10 fatcat:m6oui6ezojhvxihyoorkd7gq6a