Fast, robust, and accurate anomaly detection for multivariate time series

Anomaly detection is a topic widely studied both in Statistics and Computer Science, with an ever growing literature in both disciplines. We present a novel, fast, robust, accurate, and widely applicable semi-supervised procedure for anomaly detection in multivariate time series, (Fast, Robust, and Accurate ANomaly detection). It comprises 5 steps: smoothing, multicollinearity mitigation, dissimilarity measurement, threshold selection, identification of the causes of the anomalies. can tackle issues from different challenging contexts, where signals can be highly multicollinear, have unknown distributions, and intertwine short-lived noise with longer-lived anomalies. Using several experiments, we demonstrate the generality, low computational cost, precision, and interpretability of. In particular: (i) Using public benchmark datasets from anomaly detection, we evaluate the computational cost and performance of against the semi-supervised methods from a recent literature review, finding that is effective, broadly applicable, and that it outperforms existing approaches in anomaly detection and runtime; (ii) Using such datasets we also show that can explain the causes of the discovered anomalies; (iii) Using simulation studies, we show that is robust to several possible issues in the data; (iv) Using a case study from an industrial partner, we show that is effective.