TITLE:
A Hybrid Neural Network Model Based on Transfer Learning for Forecasting Forex Market
AUTHORS:
Salum Hassan Faru, Anthony Waititu, Lawrence Nderu
KEYWORDS:
Deep Learning, Transfer Learning, Time Series Analysis, RNN, LSTM
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.11 No.2,
March
30,
2023
ABSTRACT: The forecasting research literature has developed greatly in recent years
as a result of advances in information technology. Financial time-series tasks
have made substantial use of machine learning
and deep neural networks, but building a prediction model from scratch
takes time and computational resources. Transfer learning is growing popular in
tackling these constraints of training time and computational resources in
several disciplines. This study proposes a
hybrid base model for the financial time series prediction employing the recurrent neural network (RNN) and long-short term memory (LSTM) called
RNN-LSTM. We used random search to fine-tune the hyperparameters and compared
our proposed model to the RNN and LSTM base models and evaluate using the RMSE, MAE, and MAPE metrics. When
forecasting Forex currency pairs
GBP/USD, USD/ZAR, and AUD/NZD our proposed base model for transfer learning outperforms RNN and LSTM
base model with root mean squared errors of 0.007656, 0.165250, and
0.001730 respectively.