SCALABLE BOOTSTRAP METHOD FOR ASSESSING THE QUALITY OF MACHINE LEARNING ALGORITHMS OVER MASSIVE TIME SERIES

Described is a system for assessing the quality of machine learning algorithms over massive time series. A set of random blocks of a time series data sample of size n is selected in parallel. Then, r resamples are generated, in parallel, by applying a bootstrapping method to each block in the set of random blocks to obtain a resample of size n, where r is not fixed. Errors are estimated on the r resamples, and a final accuracy estimate is produced by averaging the errors estimated on the r resamples.