Machine-learning assisted adaptive thresholding recommendations in information technology service intelligence (itsi) for aggregated time-series data sets

Abstract

One implementation of the disclosure is directed to an Information Technology Service Intelligence (ITSI) that provides methods having operations of obtaining historical data for a metric including a plurality of time-series data sets spanning a common time period and each associated with a distinct entity, generating an aggregated time-series data set by applying an aggregation function to corresponding data points in each of the plurality of time-series data sets, selecting a seasonality pattern for the aggregated time-series data set from a plurality of candidate seasonality patterns by computing a silhouette score for each of the plurality of candidate seasonality patterns in view of the time-series data set, wherein a selected seasonality pattern has a highest silhouette score, and partitioning the aggregated time-series data set into subsequences according to the selected seasonality pattern, and generating a plurality of severity level thresholds for each subsequence.