Patent ID: 9652716
Date: 2017-05-16
CPC Classifications: G06N

Claim:
1. A method, comprising: extracting shapelets from each of a plurality of time series dimensions of multi-dimensional time series data from one or more sensors in one or more physical systems; building a plurality of decision-tree classifiers, one for each of the plurality of time series dimensions, responsive to the shapelets extracted therefrom; generating a pairwise similarity matrix between respective different ones of the plurality of time series dimensions using the shapelets as intermediaries for determining similarity; applying a feature selection technique to the pairwise similarity matrix to determine respective feature weights for each of shapelet features of the shapelets and respective classifier weights for each of the plurality of decision-tree classifiers that uses the shapelet features; interpreting different ones of the shapelets as different events, and finding frequent sequential patterns between the different events, wherein the patterns denote complex signatures for different classes of multivariate instances; and combining decisions issued from the plurality of decision-tree classifiers to generate a final verdict of classification for a time series dimension responsive to the respective feature weights and the respective classifier weights.