MVCAPA uses a penalised saving statistic and dynamic programming to consistently detect multiple sparse or dense collective anomalies in multivariate time series, with finite-sample false positive control and support for lagged and point anomalies.
A linear time method for the detection of point and collective anomalies
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Subset Multivariate Collective And Point Anomaly Detection
MVCAPA uses a penalised saving statistic and dynamic programming to consistently detect multiple sparse or dense collective anomalies in multivariate time series, with finite-sample false positive control and support for lagged and point anomalies.