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Time series envelope AR forecast-based EMD end effect processing method

A technology of time series and endpoint effects, applied in pattern recognition in signals, complex mathematical operations, instruments, etc., can solve problems such as research and no EMD endpoint effect processing methods, achieve endpoint effect suppression, solve endpoint effects, and provide performance effect

Inactive Publication Date: 2017-10-27
HARBIN ENG UNIV
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Problems solved by technology

At present, there is no research on EMD endpoint effect processing method based on enveloped AR forecast at home and abroad

Method used

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  • Time series envelope AR forecast-based EMD end effect processing method
  • Time series envelope AR forecast-based EMD end effect processing method
  • Time series envelope AR forecast-based EMD end effect processing method

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Embodiment Construction

[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0025] The present invention mainly includes firstly, using all maximum points of the time series to obtain the upper envelope of the time series through spline fitting; secondly, using the time series of the upper envelope to identify the AR model, and the upper envelope Multi-step forecasting is performed at the left and right ends of the envelope to obtain the value of the upper envelope between the left and right ends of the time series and the nearest maximum point; third, use all the minimum points of the time series to obtain by spline fitting The upper envelope of the time series; fourth, use the time series of the lower envelope to identify the AR model, perform multi-step forecasting at the left and right ends of the lower envelope, and obtain the distance between the left and right ends of the time series and the nearest min...

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Abstract

The invention provides a time series envelope AR forecast-based EMD end effect processing method, and aims at restraining the end effect of an EMD method in nonlinear instable time series processing. The method comprises the following steps of: firstly obtaining an upper envelope line of a time series through spline fitting by utilizing all the maximum value points of the time series; secondly, identifying an AR model by utilizing the upper envelope line and the time series, carrying out multistep forecast at the left and right ends of the upper envelope line to obtain values of the upper envelope between the left and right ends of the time series and the nearest maximum value point; thirdly, obtaining a lower envelope line of the time series through spline fitting by utilizing all the minimum value points of the time series; and finally, identifying the AR model by utilizing the lower envelope line and the time series, and carrying out multistep forecast at the left and right ends of the lower envelope line to obtain values of the lower envelope line between the left and right ends of the time series and the nearest minimum value point. Through above steps, the end effect in EMD processing can be restrained.

Description

technical field [0001] The invention relates to a processing method of nonlinear and non-stationary time series, in particular to an EMD endpoint effect processing method based on time series envelope AR prediction, belonging to the field of time series analysis. Background technique [0002] Empirical Mode Decomposition (EMD) is currently the most effective and widely used nonlinear non-stationary signal processing method, which was proposed by Norden.E Huang et al. in 1998. This method breaks the definition of the basic signal and frequency by the Fourier transform, and considers that the basic component of the signal is a signal called an Intrinsic Mode Function (IMF), rather than simply a sinusoidal signal. IMF must meet two basic conditions: first, the number of zero points in the entire signal interval is equal to the number of extreme points, or a difference of at most 1; second, the minimum value of the signal is less than zero and the maximum point is greater than z...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/10G06K9/00
CPCG06F17/10G06F2218/10
Inventor 黄礼敏段文洋马学文赵彬彬韩阳刘煜城
Owner HARBIN ENG UNIV
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