A fast method for estimating correlation coefficients of mass monitoring long-time series data

A long-term series, data-related technology, applied in the database field, can solve problems such as inability to use interactive query applications, sequence mean and standard deviation, long delay, etc.

Inactive Publication Date: 2018-12-11
XINJIANG INST OF ENG
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AI Technical Summary

Benefits of technology

The technical effect of this patented method described in this patents allows for quick estimation of long term sequences from multiple layers collected over time through advanced correlation coefficient estimator algorithms called Multiplex Causes Analysis).

Problems solved by technology

This patented technical problem addressed in this patents relates to efficiently handling big amounts of data with diverse types of tools that require complicated operations for efficient searching across multiple sources without slowing down any interactions between them.

Method used

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  • A fast method for estimating correlation coefficients of mass monitoring long-time series data
  • A fast method for estimating correlation coefficients of mass monitoring long-time series data
  • A fast method for estimating correlation coefficients of mass monitoring long-time series data

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Embodiment Construction

[0033] A specific embodiment of the present invention will be described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific embodiment.

[0034] Time series: Time series S is a collection of observation data arranged in chronological order, which can be expressed as S=((t 1 ,s 1 ),(t 2 ,s 2 ),...,(t n ,s n )), where s i is at timestamp t i The value on (1≤i≤n), n is the length of the time series S;

[0035] Subsequence: for any i and l satisfying 1≤i≤n-l, S i,l =(s i ,s i+1 ,...,s i+l-1 ) is a subsequence of S.

[0036] Pearson correlation coefficient: Given a time series of equal length X=(x 1 ,x 2 ,...,x n ) and Y=(y 1 ,y 2 ,...,y n ), the calculation formula of the Pearson correlation coefficient ρ(X,Y) of X and Y is as follows where μ x and μ y are the means of X and Y, respectively, such as σ x and σ y are the standard deviation...

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Abstract

The invention relates to the technical field of database and discloses a fast method for estimating correlation coefficients of mass monitoring type long-time series data, the method comprising the following steps: S1, cutting the long-time series set into sub-sequences, and distributively storing all the sub-sequences in the database; S2, fast estimating the Pearson correlation coefficients of the two long time series by a CCEA algorithm, calculating the mean and variance of the sub-sequence on the computing node and transmitting the result back to the scheduling node N0, and then estimatingthe upper and lower bounds of the Pearson correlation coefficients of the whole long time series by using the mean and variance of the sub-sequence on N0; S3, when the amount of data is too large, using an ACCEA algorithm to maintain the multi-layer summary data, and estimating the correlation coefficients of the long-time series set quickly by an iterative method. This method can be used to estimate the correlation coefficients of massive monitoring long-time series data quickly.

Description

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Claims

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Application Information

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Owner XINJIANG INST OF ENG
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