… and other ponderings in 11th-dimensional space

Barajas et al., US Patent 8,577,815 Granted

Nov 5th, 2013 | By | Category: Featured Articles, Patents, Publications
United States Patent 8,577,815
Barajas ,   et al. November 5, 2013

Method and system for concurrent event forecasting

Download: 8,577,815.pdf [text]

 

Abstract
A method and system for characterizing, detecting, and predicting or forecasting multiple target events from a past history of these events includes compressing temporal data streams into self-organizing map (SOM) clusters, and determining trajectories of the temporal streams via the clusters to predict the multiple target events. The system includes an evolutionary multi-objective optimization (EMO) module for processing the temporal data streams, which are obtained from a plurality of heterogeneous domains; a SOM module for characterizing the temporal data streams into self-organizing map clusters; and a target event prediction (TEP) module for generating prediction models of the map clusters. The SOM module employs a vector quantization method that places a set of vectors on a low-dimensional grid in an ordered fashion. The prediction models each include trajectories of the temporal data streams, and the system predicts the multiple target events using the trajectories.


Inventors: Barajas; Leandro G. (Troy, MI), Cho; Youngkwan (Los Angeles, CA), Srinivasa; Narayan (Oak Park, CA)
Applicant:
Name City State Country Type

Barajas; Leandro G.
Cho; Youngkwan
Srinivasa; Narayan
Troy
Los Angeles
Oak Park
MI
CA
CA
US
US
US
Assignee: GM Global Technology Operations LLC (Detroit, MI)
Family ID: 43899232
Appl. No.: 12/604,606
Filed: October 23, 2009

US08577815_Method and system for concurrent event forecasting_461

A method for simultaneously characterizing, detecting, and predicting multiple target events comprising: processing a plurality of temporal data streams of past occurrences of the target events using an evolutionary multi-objective optimization (EMO) module of a controller, wherein the temporal data streams are obtained asynchronously with respect to each other from a plurality of heterogeneous domains, and wherein at least some of the plurality of heterogeneous domains are of an incompatible format with the other heterogeneous domains; characterizing the temporal data streams from the EMO into map clusters via a vector quantization method using a self-organizing map (SOM) module of the controller, wherein the SOM module is adapted to map the temporal data streams from a high-dimensional space to a one-dimensional space, which places vectors one a one-dimension grid in an ordered fashion; generating prediction models of the map clusters using a target event prediction (TEP) module of the controller, wherein the prediction models each include trajectories of the temporal data streams that are represented as a string of events in the one-dimensional space; and estimating a future occurrence of the multiple target events using the trajectories from the TEP module.

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