Unsupervised time series outlier detection

Abstract

Techniques are disclosed that relate to identifying a set of outlier data points from a set of time-series data. A computer system may receive time-series data that includes a plurality of data points. The computer system scores, using a first scoring function, a plurality of candidate outlier detection models to evaluate their ability to accurately predict outlier data points in the time-series data. The computer system selects, based on results of the scoring, a selected one of the plurality of candidate outlier detection models that predicts a preliminary set of outlier data points. Weak outliers may be removed and missing outliers added to generate a final set of outlier data points. The computer system may output one or messages explaining the rationale for including one or more of the final set of outlier data points.

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