发明名称 Performing predictive analysis on usage analytics
摘要 Methods for predicting future data based on time-dependent data with increased accuracy include generating resampled datasets from a base dataset having at least one time dependent characteristic. Generating the resampled datasets includes randomly resampling data points in the base dataset to increase a pool of data for predicting future data while at least partially maintaining one or more time dependent characteristics of the base dataset. One or more embodiments apply a modified bootstrapping algorithm to the base dataset to generate the resampled datasets. Predicting the future data includes applying a time series algorithm to the resampled datasets to generate a predicted future dataset with improved accuracy by utilizing the time dependent characteristic maintained in the resampled datasets.
申请公布号 US9609074(B2) 申请公布日期 2017.03.28
申请号 US201414308311 申请日期 2014.06.18
申请人 ADOBE SYSTEMS INCORPORATED 发明人 Modarresi Kourosh
分类号 G06F17/30;H04L29/08;G06Q10/04;G06Q30/02 主分类号 G06F17/30
代理机构 Keller Jolley Preece 代理人 Keller Jolley Preece
主权项 1. A method of predictive analytics, comprising: identifying, by at least one processor, a base dataset comprising a plurality of sampled data points describing a set of events, wherein the plurality of sampled data points in the base dataset comprise a chronological order; randomly resampling, by the at least one processor, the plurality of sampled data points to obtain a plurality of resampled data points, wherein randomly resampling the plurality of sampled data points comprises randomly resampling a first sampled data point and, for each subsequent resampled data point from the plurality of resampled data points, randomly resampling a data point from a subset of the plurality of sampled data points that is chronologically at the same time as, or after, a most recent resampled data point preceding the resampled data point; generating, by the at least one processor, a plurality of resampled datasets comprising the plurality of randomly resampled data points while maintaining the chronological order of the plurality of sampled data points; and predicting, by the at least one processor, a set of data points corresponding to future events by applying a time series algorithm to the plurality of resampled datasets.
地址 San Jose CA US