Artificial Neural Network Model with PSO as a Learning Method to Predict Movement of the Rupiah Exchange Rate against the US Dollar

2021 ◽  
pp. 81
Author(s):  
Eko Verianto ◽  
Budi Sutedjo Dharma Oetomo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Pso Algorithm ◽  
Learning Method ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
The Us ◽  
Us Dollar ◽  
Artificial Neural

The movement of currency exchange rate can be predicted in the next few days, this is used by economic actors to get profit. Artificial Neural Network with the backpropagation learning method is good enough to use for forecasting time series data, it's just that in its application this method was considered to have shortcomings such as a long training time to achieve convergence. The purpose of this research is to form a Multilayer Perceptron Artificial Neural Network model with the Particle Swarm Optimization (PSO) algorithm as a learning method in the case of currency exchange rate prediction. This research produced a model that can predict the movement of the Rupiah exchange rate against the US Dollar, while the model formed was the MLP-PSO model with an error rate of 5.6168 x 10-8, slightly better than the MLP-BP model with an error rate of 6.4683 x 10-8. These results indicated that the PSO algorithm can be used as a learning algorithm in the Multilayer Perceptron Artificial Neural Network.

scholarly journals Forecasting Malaysian Exchange Rate using Artificial Neural Network

2020 ◽  
Vol 15 (2) ◽  
pp. 136-145
Author(s):  
Ikhwan Muzammil Amran ◽  
Anas Fathul Ariffin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Global Economy ◽  
Information Criterion ◽  
Foreign Exchange Rate ◽  
Kuala Lumpur ◽  
Us Dollar ◽  
Artificial Neural

In todays fast paced global economy, the accuracy in forecasting the foreign exchange rate or predicting the trend is a critical key for any future business to come. The use of computational intelligence based techniques for forecasting has been proved to be successful for quite some time. This study presents a computational advance for forecasting the Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar. A neural network based model has been used in forecasting the days ahead of exchange rate. The aims of this research are to make a prediction of Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar using artificial neural network and determine practicality of the model. The Alyuda NeuroIntelligence software was utilized to analyze and to predict the data. After the data has been processed and the structural network compared to each other, the network of 2-4-1 has been chosen by outperforming other networks. This network selection criteria are based on Akaike Information Criterion (AIC) value which shows the lowest of them all. The training algorithm that applied is Quasi-Netwon based on the lowest recorded absolute training error. Hence, it is believed that experimental results demonstrate that Artificial Neural Network based model can closely predict the future exchange rate.

scholarly journals Forecasting currency exchange rate time series with fireworks-algorithm-based higher order neural network with special attention to training data enrichment

2020 ◽  
Vol 21 (4) ◽  
Author(s):  
Kishore Kumar Sahu ◽  
Sarat Chandra Nayak ◽  
Himanshu Sekhar Behera
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Exchange Rate ◽  
Training Data ◽  
Training Dataset ◽  
Functional Link ◽  
Fireworks Algorithm ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Artificial Neural

Exchange rates are highly fluctuating by nature, thus difficult to forecast. Artificial neural networks (ANN) have proved to be better than statistical methods. Inadequate training data may lead the model to reach suboptimal solution resulting, poor accuracy as ANN-based forecasts are data driven. To enhance forecasting accuracy, we suggests a method of enriching training dataset through exploring and incorporating of virtual data points (VDPs) by an evolutionary method called as fireworks algorithm trained functional link artificial neural network (FWA-FLN). The model maintains the correlation between the current and past data, especially at the oscillation point on the time series. The exploring of a VDP and forecast of the succeeding term go consecutively by the FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other models trained similarly and produces far better prediction accuracy.

scholarly journals Quickpropagation Architecture Optimization Based on Input Pattern for Exchange Rate Prediction from Rupiah to US Dollar

2018 ◽  
Vol 5 (2) ◽  
pp. 171-184
Author(s):  
Harits Farras Zulkarnaen ◽  
Sukmawati Nur Endah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Network Architecture ◽  
Input Pattern ◽  
Neural Network Models ◽  
Input Layer ◽  
Us Dollar ◽  
Artificial Neural

Money exchange between countries was done by using exchange rates. One of the examples was the exchange between Rupiah and US Dollar. Exchange rates prediction to US Dollar was an attempt to assist all related economic actors to avoid losses during the process of decision making. The prediction could be done by using artificial neural network method. Quickpropagation was one of artificial neural network models considered suitable for prediction. Quickpropagation network architecture consisted of input layer, hidden layer, and output layer. The input layer of quickpropagation architecture could be determined by using autoregression (AR) for the input pattern. In this research, the authors aim to optimize the quickpropagation network architecture method using Nguyen-Widrow weight initialization to predict the Rupiah exchange rate to US Dollar. The research data were the exchange rate from the BI website from May 2017 to July 2017 with a total of 57 data. The test was performed by using K-Fold Cross Validation with k = 11 values for data without AR and k = 8 for AR data. The results show that quickpropagation method using AR has better performance than quickpropagation method without AR in terms of MSE training and testing. The best parameters are in alpha 0,6 and hidden neuron 5, with MSE training value 0,03272 and MSE testing 0,02873 for selling rate and at alpha 0,9 and hidden neuron 5, with MSE training value 0,03297 and MSE testing 0,02828 for buying rate with maximal epoch 100.000 and target error 0,05.

Determining Influencing Factors of Currency Exchange Rate for Decision Making in Global Economy Using MARS Method

2018 ◽  
pp. 261-273 ◽  
Author(s):  
Hasan Dinçer ◽  
Ümit Hacıoğlu ◽  
Serhat Yüksel
Keyword(s):  
Economic Growth ◽  
Decision Making ◽  
Exchange Rate ◽  
Current Account ◽  
Global Economy ◽  
Current Account Deficit ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Us Dollar ◽  
Turkish Lira

The aim of this study is to identify the determinants of US Dollar/Turkish Lira currency exchange rate for strategic decision making in the global economy. Within this scope, quarterly data for the period between 1988:1 and 2016:2 was used in this study. In addition to this aspect, 10 explanatory variables were considered in order to determine the leading indicators of US Dollar/Turkish Lira currency exchange rate. Moreover, Multivariate Adaptive Regression Splines (MARS) method was used so as to achieve this objective. According to the results of this analysis, it was defined that two different variables affect this exchange rate in Turkey. First of all, it was identified that there is a negative relationship between current account balance and the value of US Dollar/Turkish Lira currency exchange rate. This result shows that in case of current account deficit problem, Turkish Lira experiences depreciation. Furthermore, it was also concluded that when there is an economic growth in Turkey, Turkish Lira increases in comparison with US Dollar. While taking into the consideration of these results, it could be generalized that emerging economies such as Turkey have to decrease current account deficit and investors should focus on higher economic growth in order to prevent the depreciation of the money in the strategic investment decision.

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