Patent ID: 8209270
Filing Date: 2012-06-26
Classification: G06K

Abstract:
1. A computer-implemented method of generating a statistical classification model using a set of labeled time series events, wherein each time series event is labeled with a class label, wherein the classification model is configured for use by a computer system to determine a class associated with an unlabeled time series event, the method comprising: identifying, for each time series event of the set of labeled time series events, a plurality of time intervals associated with at least a first time series feature of a set of time series features, wherein each time series feature represents a feature for at least one time point of a time series event, and wherein each time interval includes a plurality of time points associated with feature values above a specified threshold of a plurality of specified thresholds associated with the at least a first time series feature; identifying, for each of at least some time series events of the set of labeled time series events, a first subset of a plurality of multi-scale features generated based on the plurality of time intervals, the multi-scale features of the first subset corresponding at least in part to a subset of space or time points within a time series event that contain feature data that distinguish the time series event as belonging to a class of time series events that corresponds to the class label; generating a statistical classification model for classifying an unlabeled time series event based on the class corresponding with the class label, the generating based at least in part on the first subset of the plurality of multi-scale features; and storing the statistical classification model in a computer-readable storage medium.