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.
Evaluating stationarity via change-point alternatives with applications to fMRI data
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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.