Generalized notion of similarities between uncertain time series

Abstract

A method for finding a distance between a plurality of time series, wherein each individual time series in the plurality of time series includes data, wherein the data is uncertain data, the method comprising: selecting at least two time series from a plurality of time series; computing a first difference value between the two series at a given instant of time; mapping the first difference value with a table of values; computing a second difference value using the table of values, wherein the second distance value is a measure of similarity between the time series.

Description

technical field [0001] The present invention relates to identifying distances between multiple time series. Background technique [0002] Distance measures for similarity search and data mining often focus on uncertain data, such as those generated from sensor networks. Recently, however, there has been a shift toward realizing that in many application domains the uncertainty of such data should be captured and accounted for. However, there aren't many ways to deal with time series or streaming data. [0003] Typically, values ​​corresponding to different time slots in the time series have different error contributions. What is needed is a technique for performing data mining tasks such as time series clustering and classification. Conventional distance measures cannot be effective for uncertain data. [0004] The paper "A framework for clustering uncertain data streams" (C.C. Aggarwal and P.S. Yu, 2008) proposes a framework for clustering uncertain data streams. The pa...

AI Technical Summary

Problems solved by technology

Efficient processing and effective use of sensor data is impossible without effective techniques for dealing with errors in uncertain data

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