scholarly journals Peramalan Nilai Tukar Rupiah Terhadap Mata Uang Negara Asia Menggunakan Metode Quantum Neural Network

2021 ◽  
Vol 20 (1) ◽  
pp. 153
Author(s):  
Putu Risanti Iswardani ◽  
Made Sudarma ◽  
Lie Jasa
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Prediction System ◽  
Research Results ◽  
Quantum Neural Network ◽  
Rate Difference ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
The World ◽  
Japanese Yen

Currency is a standardized payment instrument used all around the world. Almost each country has their own currency, and it has a variable value. Asia is one of the biggest continent in the world. Most tourists whom visit Indonesia come from Asia. Thus, that makes the most currency exchanged in Indonesia are currencies from Asia. Currency exchange rate difference between one currency and another affected by several factor. One of those factor is inflation in its country. To overcome that issue, one needs a prediction system that can be used to predict the exchange rate of currencies in the future. Quantum Neural Network is used in this research to predict Indonesian Rupiah exchange rate value to other currency in Asia. Singapore, Hongkong and Japan currencies are particularly used in this research. Results obtained from this research are accuracies. Quantum Neural Network produces 99.78% accuracy on Singapore Dollar to Indonesian Rupiah exchange rate, 99.57% on Hongkong Dollar to Indonesian Rupiah, and 99.60% on Japanese Yen to Indonesian Rupiah.

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 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 The Dependence of the CZK Exchange Rate on the Development of the GDP in the Czech Republic in the Conditions of an Expected Crisis

2021 ◽  
Vol 92 ◽  
pp. 08013
Author(s):  
Veronika Machová ◽  
Tomáš Krulický
Keyword(s):  
Neural Networks ◽  
Exchange Rate ◽  
Czech Republic ◽  
Value Added ◽  
The Czech Republic ◽  
The Past ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
The World ◽  
Rate Development

Research background: In the past, some studies proved that the development of a currency exchange rate predicts the development of the whole national economy. The monetary market overtakes the development of the actual economy for a few months. Does this apply also in the case of the Czech koruna, in the era of the global Coronavirus pandemics and in the world affected by the pandemics? Purpose of the article: The main objective is to analyze a dependence of the Czech koruna (CZK) to Euro (EUR) exchange rate development on gross domestic product of the Czech Republic in the conditions of an expected crisis. Methods: The data used of the analysis are represented by the information about the CZK and EUR exchange rate from the beginning of 1999 to the 15th June 2020 and by the quarterly development of the Czech GDP. To measure the dependence and predict the development of the GDP based on the CZK exchange rate development, the method of AI is used, namely the regression analysis using the artificial neural networks. Findings & Value added: The effect of EUR/CZK on GDP can be quantified reaching around 31%. It is assumed that the GDP will fall significantly in 2020 with a certain growth only being possibly expected in 2021 (even more significantly in the second quarter of 2021). Due to the GDP development, the development of the EUR/CZK could then be forecasted as well.

Does Artificial Neural Network Forecast Better for Excessively Volatile Currency Pairs?

2017 ◽  
Vol 10 (2) ◽  
pp. 47-61
Author(s):  
Sarveshwar Kumar Inani ◽  
Manas Tripathi ◽  
Saurabh Kumar
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Network Architecture ◽  
Back Propagation ◽  
Neural Network Architecture ◽  
Forecast Horizon ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Daily Data ◽  
The Neural Network

This study predicts the exchange rates for three currency pairs (USD-INR, GBP-INR, and EUR-INR). We have used multi-layer perceptron (MLP) neural network architecture based on feed-forward with back-propagation learning method.  The sample of the study covers daily data for the period from January 2009 to January 2016. The findings of the study confirm that the neural network predicts better for more volatile currency pairs (GBP-INR and EUR-INR) as compared to a less volatile currency pair (USD-INR). The study further observes that the optimal forecast horizon for the neural network model should be equal to the optimal lag length used in the construction of the model. This study aims to contribute in the area of foreign exchange forecasting. Exchange rate plays a crucial role in the macro-economy of a country. Hence, prediction of currency exchange rate becomes imperative for various stakeholders such as government, the central bank, and investors to maximize the returns and minimize the risk in their decision-making.

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