Comparing RMB Exchange Rate Forecasting Accuracy based on Dynamic BP Neural Network Model and the ARMA Model

Ye, Zhiqiang; Ren, Xiang

doi:10.4172/2168-9458.1000161

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…So they may fail to come up with accurate predictions. Ye et al, in [4] have proposed a dynamic backpropagation neural network model and an Auto Regressive Moving Average (ARMA) model to forecast the RMB exchange rate. The results reveal the better performance of ANN over ARMA for both the trend and deviation prediction.…”

Section: Introductionmentioning

confidence: 99%

Utility of a Shuffled Differential Evolution algorithm in designing of a Pi-Sigma Neural Network based predictor model

Dash

Rautray

Dash

2020

ACI

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Since the last few decades, Artificial Neural Networks have been the center of attraction of a large number of researchers for solving diversified problem domains. Due to its distinguishing features such as generalization ability, robustness and strong ability to tackle nonlinear problems, it appears to be more popular in financial time series modeling and prediction. In this paper, a Pi-Sigma Neural Network is designed for foretelling the future currency exchange rates in different prediction horizon. The unrevealed parameters of the network are interpreted by a hybrid learning algorithm termed as Shuffled Differential Evolution (SDE). The main motivation of this study is to integrate the partitioning and random shuffling scheme of Shuffled Frog Leaping algorithm with evolutionary steps of a Differential Evolution technique to obtain an optimal solution with an accelerated convergence rate. The efficiency of the proposed predictor model is actualized by predicting the exchange rate price of a US dollar against Swiss France (CHF) and Japanese Yen (JPY) accumulated within the same period of time.

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Section: Introductionmentioning

confidence: 99%