HIGH-FREQUENCY EXCHANGE-RATE PREDICTION WITH AN ARTIFICIAL NEURAL NETWORK

2012 ◽  
Vol 19 (3) ◽  
pp. 170-178 ◽  
Author(s):  
Taufiq Choudhry ◽  
Frank McGroarty ◽  
Ke Peng ◽  
Shiyun Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
High Frequency ◽  
Rate Prediction ◽  
Artificial Neural ◽  
Exchange Rate Prediction

scholarly journals A survey on exchange rate prediction using neural network based methods

2018 ◽  
Vol 7 (2.6) ◽  
pp. 64 ◽  
Author(s):  
Pragyan Paramita Barik ◽  
Smruti Rekha Das ◽  
Debahuti Mishra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Network Models ◽  
Rate Prediction ◽  
Artificial Neural ◽  
Consistent Performance ◽  
Artificial Neural Network Ann ◽  
Exchange Rate Prediction

Forecasting exchange rate has always been in demand as it is very important for the international traders to predict how their money will perform against other currencies. So different methods have been applied by the researchers to accurately predict the exchange rates so that it can assist in taking decision while trading. From all the models the Artificial Neural Network (ANN) has given consistent performance in prediction by overcoming the limitations of other models and has outperformed all the models in terms of efficiency. The evolution of ANN is remarkable. In this paper, we have given the performance of different network models used by researchers to predict the exchange rates of major currencies in the future.

Rainfall Estimation Using Neuron-Adaptive Higher Order Neural Networks

2010 ◽  
pp. 159-186 ◽  
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Frequency ◽  
Higher Order ◽  
Rainfall Estimation ◽  
Test Error ◽  
Artificial Neural ◽  
Training Error ◽  
Optimum Model ◽  
Artificial Neural Network Ann

Real world data is often nonlinear, discontinuous and may comprise high frequency, multi-polynomial components. Not surprisingly, it is hard to find the best models for modeling such data. Classical neural network models are unable to automatically determine the optimum model and appropriate order for data approximation. In order to solve this problem, Neuron-Adaptive Higher Order Neural Network (NAHONN) Models have been introduced. Definitions of one-dimensional, two-dimensional, and n-dimensional NAHONN models are studied. Specialized NAHONN models are also described. NAHONN models are shown to be “open box”. These models are further shown to be capable of automatically finding not only the optimum model but also the appropriate order for high frequency, multi-polynomial, discontinuous data. Rainfall estimation experimental results confirm model convergence. We further demonstrate that NAHONN models are capable of modeling satellite data. When the Xie and Scofield (1989) technique was used, the average error of the operator-computed IFFA rainfall estimates was 30.41%. For the Artificial Neural Network (ANN) reasoning network, the training error was 6.55% and the test error 16.91%, respectively. When the neural network group was used on these same fifteen cases, the average training error of rainfall estimation was 1.43%, and the average test error of rainfall estimation was 3.89%. When the neuron-adaptive artificial neural network group models was used on these same fifteen cases, the average training error of rainfall estimation was 1.31%, and the average test error of rainfall estimation was 3.40%. When the artificial neuron-adaptive higher order neural network model was used on these same fifteen cases, the average training error of rainfall estimation was 1.20%, and the average test error of rainfall estimation was 3.12%.

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