Minimum mean distance-based dynamic time warping method

Abstract

The invention discloses a minimum mean distance-based dynamic time warping method. The method comprises the following steps of: 1) creating a mean distance matrix and a path length matrix for two time sequences; and 2) solving dynamic planning of a formula (as shown in the specification): 2.1) carrying out initialization, 2.2) carrying out assignment on the first line and first column of the mean distance matrix (as shown in the specification), carrying out weighted array on the first line and neighbors, in the same line, at the former column to calculate an arithmetic mean, and carrying out weighted array on the first column and the neighbors, in the same column, of the former line to calculate an arithmetic mean, 2.3) carrying out assignment on the lines except the first line and the columns except the first column of the mean distance matrix (as shown in the specification), combining three adjacent points, adjacent to the current point, of the former line and the former column and calculating arithmetic means, selecting the minimum mean of the obtained three means to serve as a mean distance value of the current point, and adding 1 to a path length value of the current point on the basis of a length value of the selected adjacent point, and 2.4) outputting a formula (as shown in the specification) and 1 (n, m). The minimum mean distance-based dynamic time warping method provided by the invention is relatively low in false accept rate.

Description

technical field [0001] The invention relates to a dynamic time warping method. Background technique [0002] Distance is a common method to measure the dissimilarity (similarity) of two time series. Since the distribution of sequence values ​​on the time axis will shift and expand, direct point-to-point calculations will have the risk of deviating from common sense judgments. For example, for two sequences (a, a, b, b, a) and (a, b, b, a, a) patterns, it needs to be judged to be consistent in practical application or engineering practice, but the value calculated by using distance is 2|a-b|. Using the traditional dynamic time warping algorithm to calculate the distance value is 0, which is more in line with the needs of actual engineering. However, the traditional dynamic time warping algorithm also has disadvantages. For example, consider using the dynamic time warping distance to calculate the matching of the following three segments of DC signals: [0003] A=(0.1,0.1)...

AI Technical Summary

Problems solved by technology

However, the traditional dynamic time warping algorithm also has disadvantages. For example, consider using the dynamic time warping distance to calculate the matching of the following three segments of DC signals:

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