Patent ID: 9727533
Date: 2017-08-08
CPC Classifications: G06F,G06K

Claim:
1. A computer-implemented method of detecting anomalies in time series data, comprising: modeling, by a computing device having a processor and a memory, a time series using a linear regression framework; representing, by the processor, the time series as a sum of a signal portion and a noise, wherein the signal portion corresponds to time-dependent data, and the noise removes time dependence from the data; calculating, by the processor, for a specified time, a variance of a difference between a value of the time series predicted by the linear regression framework and a value of the signal portion; determining, by the processor, a threshold based on the calculated variance; and raising, by the processor, an alarm when a difference between a value of the time series predicted by the linear regression framework and an observed value of the time series for the specified time is larger than the threshold; and allocating computing resources to process a workload based on the time series predicted by the linear regression framework, wherein the allocating includes: