Neural Network for Forecasting High Price and Low Price on Foreign Exchange Market
- Resource Type
- Conference
- Authors
- Chinprasatsak, Krin; Niparnan, Nattee; Sudsang, Attawith
- Source
- 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2020 17th International Conference on. :461-465 Jun, 2020
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Forecasting
Artificial neural networks
Time series analysis
Exchange rates
Bayes methods
Stochastic processes
machine learning
artificial neural network
bayesian regularization
empirical mode decomposition
stochastic time strength function
stochastic time strength neural network
radial basis function neural network
foreign exchange rate
prediction
- Language
This research compares 4 neural networks from the original researches (I. Backpropagation Neural Network II. Bayesian Regularized Neural Network III. Empirical Mode Decomposition Stochastic Time Strength Neural Network IV. Random Data-time Effective Radial Basis Function Neural Network) and 2 proposed neural networks (I. Empirical Mode Decomposition Random Data-time Effective Radial Basis Function Neural Network II. Empirical Mode Decomposition Random Data-time Effective Bayesian Regularized Neural Network) for predicting the exchange rate of EUR/USD currency pairs using input as a technical indicator and evaluating the networks with trading simulations consisting of investment strategies, risk management methods and financial management principles. The experiments show that the proposed neural networks yield higher returns than the original researches.