Hybrid Intelligent Forecasting Model Based on Empirical Mode Decomposition, Support Vector Regression and Adaptive Linear Neural Network

2005 ◽  
pp. 324-327 ◽  
Author(s):  
Zhengjia He ◽  
Qiao Hu ◽  
Yanyang Zi ◽  
Zhousuo Zhang ◽  
Xuefeng Chen
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Forecasting Model ◽  
Model Based ◽  
Mode Decomposition ◽  
Linear Neural Network

Forecasting of stock price index using support vector regression with multivariate empirical mode decomposition

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanmei Huang ◽  
Changrui Deng ◽  
Xiaoyuan Zhang ◽  
Yukun Bao
Keyword(s):  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Stock Price ◽  
Price Index ◽  
Support Vector ◽  
Modeling Framework ◽  
Content Type ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Stock Price Index

Purpose Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables. Design/methodology/approach Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model. Findings The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets. Originality/value This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

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