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Financial time series prediction method based on integrated empirical mode decomposition and 1-norm support vector machine quantile regression

A financial time series and empirical mode decomposition technology, applied in the financial field, can solve problems such as gaps, inconvenient risk control for investors, and lack of price change descriptions

Inactive Publication Date: 2016-02-24
BEIJING UNIV OF CHEM TECH
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Problems solved by technology

Although the prediction accuracy of these intelligent models is higher than that of traditional models, there is still a certain gap in applying the results of their predictions to decision-making
Moreover, these forecasts are based on the average value of the price, and lack the description of the various possibilities of price changes, which is not convenient for investors to carry out risk control

Method used

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  • Financial time series prediction method based on integrated empirical mode decomposition and 1-norm support vector machine quantile regression
  • Financial time series prediction method based on integrated empirical mode decomposition and 1-norm support vector machine quantile regression
  • Financial time series prediction method based on integrated empirical mode decomposition and 1-norm support vector machine quantile regression

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Embodiment approach

[0053] The second step: use the 1-norm support vector machine quantile regression model to decompose the N+1 sequences, and establish 9 sets of quantile prediction functions for each sequence, that is, τ=0.1,0.2,...,0.9 There are 9 quantiles in total, and a total of 9×(N+1) prediction functions are obtained. use f τ,j ,(j=1,2,...,N) represents the jth eigenmode function sequence c of the quantile τ j,t predictive function, f τ,r Represents the prediction function for the residuals. Its specific implementation method is as follows:

[0054] (1) According to the autocorrelation analysis of the time series, the lag period l (l<T) is determined by the Schwarz minimization principle (Bayesian information volume).

[0055] Take the original time series x t As an example, establish its p-order autoregressive model (AR(p)):

[0056] x t = β 0 +β 1 x t-1 +β 2 x t-2 +...+β p x t-p +ε t

[0057] The maximum likelihood function value L is obtained, then the Bayesian inform...

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Abstract

The invention belongs to the field of financial risk management, in particular to a time series probability distribution prediction method based on integrated empirical mode decomposition and nonlinear quantile regression. The method comprises the following steps: firstly, carrying out the integrated empirical mode decomposition on a financial price time series to obtain components with high regularity under different scales; secondly, independently predicting each component by 1-norm support vector machine quantile regression to obtain all quantile prediction results of each component; and thirdly, taking each quantile as a statistics target, independently adding the prediction result of each qnantile of each component, and integrating all prediction results to obtain the preduction result of each quantile so as to obtain the financial time series probability distribution prediction. The method provided by the invention can effectively predict a change probability of financial price, and can be applied to financial risk management and investment practice.

Description

technical field [0001] The invention belongs to the field of finance, and in particular relates to a method for predicting the probability distribution of prices and yields of various securities. Background technique [0002] Financial time series mainly includes data such as prices and yields of various securities. Although price changes are highly uncertain, there are always some laws in their changes. For example, some experienced securities investors can use technical indicators to predict the rise and fall of prices and profit from them. Discovering these laws and using them to predict price changes is of great significance for helping financial institutions or ordinary investors manage risks scientifically and make investment decisions. [0003] Financial time series often have non-stationary and nonlinear characteristics, and it is difficult for traditional linear models to capture the laws. Currently commonly used nonlinear time series forecasting models include n...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q40/06
CPCG06Q10/04G06Q40/06
Inventor 余乐安杨泽斌汤铃
Owner BEIJING UNIV OF CHEM TECH
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