Using delayed autocorrelation to improve the predictive scaling of computing resources

Abstract

Techniques are described for filtering and normalizing training data used to build a predictive auto scaling model used by a service provider network to proactively scale users' computing resources. Further described are techniques for identifying collections of computing resources that exhibit suitably predictable usage patterns such that a predictive auto scaling model can be used to forecast future usage patterns with reasonable accuracy and to scale the resources based on such generated forecasts. The filtering of training data and the identification of suitably predictable collections of computing resources are based in part on autocorrelation analyses, and in particular on 'delayed' autocorrelation analyses, of time series data, among other techniques described herein.