Hybrid Forecasting of Exchange Rate by Using Chaos Wavelet SVM-Markov Model and Grey Relation Degree

TL;DR

This paper introduces a hybrid chaos wavelet SVM-Markov model combined with grey relation degree for improved short-term exchange rate forecasting, demonstrating superior performance over traditional methods.

Contribution

The paper develops a novel hybrid forecasting model integrating chaos wavelet, SVM, and Markov processes with grey relation degree for enhanced accuracy.

Findings

Two-stage combination model outperforms normal models.

Grey relation-based combination methods achieve high forecast accuracy.

The proposed approach improves short-term exchange rate predictions.

Abstract

This paper proposes an exchange rate forecasting method by using the grey relative combination approach of chaos wavelet SVM-Markov model. The problem of short-term forecast of exchange rate by using the comprehensive method of the phase space reconstitution and SVM method has been researched. We have suggested a wavelet-SVR-Markov forecasting model to predict the finance time series and demonstrated that can more improve the forecasting performance by the rational combination of the forecast results through various combinational tests. Our test result has been showed that the two-stage combination model is more excellent than the normal combination model. Also we have comprehensively estimated the combination forecast methods according to the forecasting performance indicators.The estimated result have been shown that the combination forecast methods on the basic of the degree of grey relation and the optimal grey relation combination have fine forecast performance.