Time series data nearest-neighbor classifying method based on subsection orthogonal polynomial decomposition

Abstract

The invention discloses a time series data nearest-neighbor classifying method based on subsection orthogonal polynomial decomposition. The time series data nearest-neighbor classifying method includes dividing a time sequence into subsequences comprising complete fluctuation trends on the basis of time sequence coded identification turning points; extracting Chebyshev coefficient as subsequence features by means of a first type Chebyshev polynomial decomposition subsequences, and constructing subsequence feature vectors; finally in the nearest-neighbor classifier, classifying by the dynamic planning algorithm based on local mode matching as distance metric function. Classifying accuracy and efficiency are superior to other nearest-neighbor classifiers to the great extent, and the time series data nearest-neighbor classifying method plays an important role in daily activity of people and industrial production, such as in applications of banking transactions, traffic control, air quality and temperature monitoring, industrial process monitoring, medical diagnosis and the like, massive sampling data or high-speed dynamic data can be classified and predicted, abnormalities can be detected and online modes are identified.

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. In order to make full use of massive time-series data, the industry usually needs to classify it in order to discover valuable information and knowledge. Therefore, time series classification methods have a wide range of application requirements in the industry. [0003] At present, the commonly used classifiers in the industry include artificial neural networks, support vector machines, naive Bayesi...

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

Images