Time-series data analysis system, method, and program

TL;DR: In this article, the abnormal degree is calculated as a Kullback-Leibler distance by calculation of negative entropy, and a coarse accuracy matrix which is an inverse matrix is generated from each correlation coefficient matrix by algorithm of graphic lasso.

Abstract: PROBLEM TO BE SOLVED: To heighten detection accuracy of the abnormal degree of time-series data. SOLUTION: First of all, each correlation coefficient matrix is generated, relative to each of time-series data for a test and normal time-series data for reference. Thereafter, a coarse accuracy matrix which is an inverse matrix is generated from each correlation coefficient matrix by algorithm of graphic lasso. When the accuracy matrix is acquired, a neighborhood probability distribution can be described preferably by a multivariate Gauss model, relative to each of the time-series data for the test and the normal time-series data for reference. Then, the abnormal degree is calculated as a Kullback-Leibler distance by calculation of negative entropy. This technique is advantageous, from the viewpoint that a function for estimating a neighborhood graph structure on the periphery of each sensor automatically from data is integrated into an abnormality detection procedure of the time-series data based on a neighborhood preservation principle, and that further a theoretically consistent abnormal degree is given based on direct comparison of a local statistical model. COPYRIGHT: (C)2010,JPO&INPIT