Method and system for detecting difference between plural observed results

a technology of plural observed results and detection methods, applied in the direction of denominational number representation computation, testing/monitoring control systems, instruments, etc., can solve the problems of difficult to determine the reference value of a variable for detecting anomalies, and it is unrealistic to assume that enough experienced engineers are available in every field
US20080010330A1Inactive Publication Date: 2008-01-10IBM CORP
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Patent Information

Authority / Receiving Office
US · United States
Current Assignee / Owner
IBM CORP
Publication Date
2008-01-10
Estimated Expiration
Not applicable · inactive patent

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Abstract

A method and system for analyzing time series data. In an embodiment, a loop is executed and terminated upon a specified maximum number of iterations of the loop being performed or upon a difference between scores in successive iterations of the loop not being greater than a specified tolerance, wherein the score in each iteration is calculated as function of an absolute value of a difference between respective cumulative probability values of first and second cumulative probability distributions which are generated from respectively first and second time series data sets. In an embodiment, time series data is processed in a sequence of time periods, wherein a combined cumulative probability distribution is generated in each time period by combining a cumulative probability distribution of new time series data with previously combined cumulative probability distribution data according to a ratio of the number of new to previous observed values.
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Description

FIELD OF THE INVENTION

[0001] The present invention relates to a method and system for detecting a difference between a set of observed results, and in particular, the present invention relates to a method and system for statistically analyzing the difference between time series data sets.BACKGROUND OF THE INVENTION

[0002] Detecting and analyzing anomalies of dynamic systems is an important technical challenge in various areas in the manufacturing industry. For example, anomaly detection in a production line has been of particular importance, and a lot of statistical techniques have been developed for quality control purposes. However, most of the traditional statistical quality control techniques are based on a strong assumption of multivariate normal distribution. Unless the system of interest is relatively static and stationary, the distribution of data is far from the normal distribution in general. This is especially the case in the analysis of automotives, where the system is high...

Claims

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