FOREX Prices Prediction Using Deep Neural Network and FNF

One of the largest financial markets on the planet is the foreign exchange (FOREX) market. Banks, retail traders, businesses, and individuals trade more than $5.1 trillion in FOREX daily. It is very challenging to predict prices in advance due to the market's complex, volatile, and highly fluctuating nature. In this study, the new FOREX Normalization Function (FNF) is proposed and used with different models to predict the prices of the AUD/USD, EUR/USD, USD/JPY, CHF/INR, USD/CHF, AUD/JPY, USD/CAD, and GBP/USD. Two models are proposed in this study. The first model contains FNF as a normalization and feature extractor, followed by a Convolutional Neural Network (CNN). The second model utilizes FNF and a Support Vector Regressor(SVR). The forecasts are set for a one-day timeframe, with predictions made for 1, 3, 7, and 15 days ahead. The efficient ability of the proposed method to solve the FOREX prediction problem is proven by performing experiments on nine real-world datasets from different currencies. Additionally, the models are evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE). Applying the presented models to 9 different datasets improved the results by an average between 0.5% and 58% of MAE.

@article{Moustafa2023,
  title     = {FOREX Prices Prediction Using Deep Neural Network and FNF},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Asmaa M. Moustafa and Mohamed Waleed Fakhr and Fahima A. Maghraby},
  doi       = {10.14569/IJACSA.2023.0141187},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141187}
}