Important point-based time sequence fixed segmentation algorithm

Abstract

The invention relates to an important point-based time sequence fixed segmentation algorithm. According to the main technical characteristics, the algorithm comprises the steps of performing normalization processing on time sequence data; calculating segmentation fitting errors and performing sorting according to priorities of the segmentation fitting errors from high to low; extracting first k segments with maximum fitting errors and performing sorting from long to short according to segment lengths of a time sequence; extracting first two segments sorted according to the segment lengths, and performing fitting preprocessing; and comparing symbol and value relationships of maximum values and minimum values of the fitting errors in segmentation, determining important points to perform segmentation, progressively increasing the number of the important points, and performing loop iteration until a fixed segment number is reached. The algorithm is reasonable in design, greatly reduces the fitting errors, improves the segmentation efficiency, has very good fitting effect and adaptability, and can be widely used in the fields of time sequence prediction, clustering, anomaly detection and the like.

Description

technical field [0001] The invention belongs to the technical field of intelligent information processing, in particular to a time series fixed segmentation algorithm based on important points. Background technique [0002] Time series refers to the ordered collection of observation records arranged in chronological order, which widely exists in the fields of business, economics, scientific engineering and social sciences. In recent years, data mining research on time series data has received widespread attention, including association rule mining, similarity query, pattern discovery, anomaly detection, etc. As time goes by, time series usually contains a large amount of data. How to conduct statistics and analysis on these time series data and find some valuable information and knowledge has always been an issue of interest to users. However, due to the massive and complex characteristics of time series data, data mining directly on the time series will not only cost a lot...

AI Technical Summary

Problems solved by technology

However, the defect of this method is that the degree of compression cannot be selected according to the user's needs, because this method uses a recursive call method to decompose the leftmost sequence until the fitting error is less than a certain value specified by the user. The need to find the most important specified number of points

[0008] The time series fixed segment number segmentation algorithm based on important points proposed by Chen Ran uses the fitting error of each segment as the priority standard, and sets the error threshold as the input parameter, but the parameter value of the error threshold is not easy to estimate

[0009] In summary, the existing time series important point segmentation algorithm has a lot of room for improvement in the accuracy of fitting error and time efficiency

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