Machine-learning assisted adaptive thresholding recommendations in information technology service intelligence (itsi)

Abstract

One implementation of the disclosure is directed to an Information Technology Service Intelligence (ITSI) that provides methods for automatically generating one or more severity level thresholds for a metric represented by a time-series data set through adaptive thresholding. One implementation may include operations to retrieve a historical time-series data set that represents a KPI or metric being monitored, perform an adaptive threshold generation process for the time-series data set that includes automatically selecting a seasonality pattern that corresponds to the time-series data set and determining a set of thresholds based thereon. Selecting a seasonality pattern may involve, for each candidate seasonality pattern, computing a silhouette score that indicates how closely a candidate seasonality pattern corresponds to the time-series data set. Based on the selected seasonality pattern, a plurality of severity level thresholds may be determined using a statistical computation such as standard deviation, quantile, percentile, range, etc.