A Hybrid Forecast of Exchange Rate based on Discrete Grey-Markov and Grey Neural Network Model

TL;DR

This paper introduces a hybrid exchange rate forecasting model combining discrete grey-fuzzy Markov and grey neural network techniques, significantly outperforming traditional models in accuracy.

Contribution

The paper presents a novel hybrid model that integrates grey-fuzzy Markov and grey neural networks, enhancing forecast performance over existing grey-Markov and neural network models.

Findings

Hybrid model outperforms traditional grey-Markov and neural network models.

Optimal weight determination via grey relation degree improves forecast accuracy.

Simulation results confirm the effectiveness of the proposed hybrid approach.

Abstract

We propose a hybrid forecast model based on discrete grey-fuzzy Markov and grey neural network model and show that our hybrid model can improve much more the performance of forecast than traditional grey-Markov model and neural network models. Our simulation results are shown that our hybrid forecast method with the combinational weight based on optimal grey relation degree method is better than the hybrid model with combinational weight based minimization of error-squared criterion.