Gu, Q., Dai, Q., Yu, H. and Ye, R. (2021) Integrating Multi-Source Transfer Learning, Active Learning and Metric Learning Paradigms for Time Series Prediction. Applied Soft Computing, 109, Article ID: 107583.
https://doi.org/10.1016/j.asoc.2021.107583

has been cited by the following article:

• 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.