Providing interpretability for multivariate time-series data anomaly detection

An apparatus for providing interpretability for multivariate time-series data anomaly detection, the multivariate time-series data anomaly detection being performed, through a multivariate time-series data anomaly detection model, for a multivariate time-series data formed by multiple time-series data, and the apparatus comprising:

• at least one processor;
• and
• a memory storing computer-executable instructions that, when executed, cause the at least one processor to:
• obtain, from the multivariate time-series data anomaly detection model, an anomaly detection result indicating at least an anomaly period,
• determine an anomaly period correlation metric of the multiple time-series data in the anomaly period,
• determine a trace-back period correlation metric of the multiple time-series data in a trace-back period before the anomaly period,
• identify, based on a difference between the anomaly period correlation metric and the trace-back period correlation metric, at least one time-series data pair having abnormal correlation in the anomaly period from the multiple time-series data, and
• provide interpretive content for the anomaly detection result, the interpretive content indicating at least the at least one time-series data pair.

A computer program product for providing interpretability for multivariate time-series data anomaly detection, the multivariate time-series data anomaly detection being performed, through a multivariate time-series data anomaly detection model, for a multivariate time-series data formed by multiple time-series data, and the computer program product comprising a computer program that is executed by at least one processor for:

• obtaining, from the multivariate time-series data anomaly detection model, an anomaly detection result indicating at least an anomaly period;
• determining an anomaly period correlation metric of the multiple time-series data in the anomaly period;
• determining a trace-back period correlation metric of the multiple time-series data in a trace-back period before the anomaly period;
• identifying, based on a difference between the anomaly period correlation metric and the trace-back period correlation metric, at least one time-series data pair having abnormal correlation in the anomaly period from the multiple time-series data;
• and
• providing interpretive content for the anomaly detection result, the interpretive content indicating at least the at least one time-series data pair.