Fast automated detection of seasonal patterns in time series data without prior knowledge of seasonal periodicity

Abstract

A processing system receives a time series of values of a first metric corresponding to computing system performance. A computation module calculates an autocorrelation function (ACF) based on the time series of values across a set of values of tau. The spacing between each consecutive pair of values in the set of values of tau increases as tau increases. A local maxima extraction module identifies local maxima of the calculated ACF. A period determination module determines a significant period based on spacing between the local maxima and selectively outputs the significant period as a periodicity profile. A baseline profile indicating normal behavior of the first metric is generated based on the periodicity profile. An anomaly identification module selectively identifies an anomaly in present values of the first metric in response to the present values deviating outside the baseline profile.