Time sequence similarity measurement method based on self-adaptive piecewise statistical approximation

Abstract

The invention discloses a time sequence similarity measurement method based on self-adaptive piecewise statistical approximation. The method comprises the following steps of firstly, segmenting a time sequence into subsequences containing complete fluctuation trends based on time sequence coded identification turning points; secondly, extracting various statistical characteristics of each subsequence in sequence so as to configure local pattern character vectors; and lastly, computing a distance between the local pattern character vectors by utilizing a normalized distance so as to realize local pattern matching, and using the local pattern matching as a subprogram of a dynamic programming algorithm so as to realize global pattern matching. The time sequence similarity measurement method is better than the other measurement method in the aspects of measurement precision and computational efficiency to a larger extent, and plays an important role in daily activities and industrial production of people, such as similarity search, classification, clustering, predication, anomaly detection, on-line pattern recognition and the other processing of large-scale sampling data or high-speed dynamic data flow in banking transaction, traffic control, air quality and temperature monitoring, industrial flow monitoring, medical diagnosis and the other application.

Description

technical field [0001] The present invention relates to the fields of database, data mining, machine learning, information retrieval, etc., and especially relates to time series data analysis and mining. Background technique [0002] Time series widely exist in people's daily life and industrial production, such as real-time transaction data of funds or stocks, daily sales data in the retail market, sensor monitoring data in the process industry, astronomical observation data, aerospace radar, satellite monitoring data, real-time Weather temperature and air quality index, etc. The industry has proposed many time series analysis methods so far, including similarity query methods, classification methods, clustering methods, forecasting methods, anomaly detection methods, etc. Among them, many methods need to judge the similarity of time series, such as kNN classifier, k-means clustering method, etc. Therefore, the time series similarity measurement method has a wide range of ...

AI Technical Summary

Problems solved by technology

Such methods include segmented aggregation approximation, segmented linear approximation, symbolic aggregation approximation, singular value decomposition, principal component analysis, etc. The first three methods need to segment the original time series first, and then process each sub-segment separately : The segmented aggregation approximation is to calculate the average value of each segment; the segmented linear approximation is to do line segment fitting on each segment; The extracted features are relatively single, which makes it less capable of expressing time series fluctuation patterns

Singular value decomposition and principal component analysis are realized by performing a unified eigenmatrix decomposition on all time series; the typical defects of these two types of methods are that they have high computational complexity, and the decomposition process can only be done in memory, and the data Very low scalability at scale

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