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.
References
[1]
Cirillo, S., Lloyd, S. and Nordin, P. (2014) Evolving Intraday Foreign Exchange Trading Strategies Utilizing Multiple Instruments Price Series. https://arxiv.org/abs/1411.2153v1
[2]
Anastasakis, L. and Mort, N. (2009) Exchange Rate Forecasting Using a Combined Parametric and Nonparametric Self-Organising Modelling Approach. Expert Systems with Applications, 36, 12001-12011. https://doi.org/10.1016/j.eswa.2009.03.057
[3]
Islam, M.S., Hossain, E., Rahman, A., Hossain, M.S. and Andersson, K. (2020) A Review on Recent Advancements in FOREX Currency Prediction. Algorithms, 13, 186. https://doi.org/10.3390/a13080186
[4]
Tay, F.E. and Cao, L. (2001) Application of Support Vector Machines in Financial Time Series Forecasting. Omega, 29, 309-317. https://doi.org/10.1016/S0305-0483(01)00026-3
[5]
Zhang, N., Lin, A. and Shang, P. (2017) Multidimensional k-Nearest Neighbor Model Based on EEMD for Financial Time Series Forecasting. Physica A: Statistical Mechanics and Its Applications, 477, 161-173. https://doi.org/10.1016/j.physa.2017.02.072
[6]
Chen, H., Xiao, K., Sun, J. and Wu, S. (2017) A Double-Layer Neural Network Framework for High-Frequency Forecasting. ACM Transactions on Management Information Systems, 7, 4. https://doi.org/10.1145/3021380
[7]
Henrique, B.M., Sobreiro, V.A. and Kimura, H. (2019) Literature Review: Machine Learning Techniques Applied to Financial Market Prediction. Expert Systems with Applications, 124, 226-251. https://doi.org/10.1016/j.eswa.2019.01.012
[8]
Balabanov, T., Zankinski, I. and Dobrinkova, N. (2012) Time Series Prediction by Artificial Neural Networks and Differential Evolution in Distributed Environment. Large-Scale Scientific Computing 8th International Conference, LSSC 2011, Sozopol, 6-10 June 2011, 198-205. https://doi.org/10.1007/978-3-642-29843-1_22 https://link.springer.com/chapter/10.1007/978-3-642-29843-1_22
[9]
Rehman, M., Khan, G.M. and Mahmud, S.A. (2014) Foreign Currency Exchange Rates Prediction Using CGP and Recurrent Neural Network. IERI Procedia, 10, 239-244. https://doi.org/10.1016/j.ieri.2014.09.083
[10]
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
[11]
Nguyen, T.T. and Yoon, S. (2019) A Novel Approach to Short-Term Stock Price Movement Prediction Using Transfer Learning. Applied Sciences (Switzerland), 9, 22. https://doi.org/10.3390/app9224745
[12]
Cem Kadilar, D., Şdmşek, M., Gör Çağdaş Hakan Aladağ, A., üniversitesi, H., Fakültesi, F. and Bölümü, D. (2009) Forecasting the Exchange Rate Series with Ann: The Case of Turkey. Istanbul University Econometrics and Statistics e-Journal, 9, 17-29. https://dergipark.org.tr/en/pub/iuekois/issue/8991/112073
[13]
Hochreiter, S. (1998) The Vanishing Gradient Problem during Learning Recurrent Neural Nets and Problem Solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6, 107-116. https://doi.org/10.1142/S0218488598000094
[14]
Ni, L., Li, Y., Wang, X., Zhang, J., Yu, J. and Qi, C. (2019) Forecasting of Forex Time Series Data Based on Deep Learning. Procedia Computer Science, 147, 647-652. https://doi.org/10.1016/j.procs.2019.01.189
[15]
Yıldırım, D.C., Toroslu, I.H. and Fiore, U. (2021) Forecasting Directional Movement of Forex Data Using LSTM with Technical and Macroeconomic Indicators. Financial Innovation, 7, 1-36. https://doi.org/10.1186/s40854-020-00220-2
[16]
Hossain, M.A., Karim, R., Thulasiram, R., Bruce, N.D. and Wang, Y. (2019) Hybrid Deep Learning Model for Stock Price Prediction. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bengaluru, 18-21 November 2018, 1837-1844. https://doi.org/10.1109/SSCI.2018.8628641
[17]
Wu, D., Wang, X. and Wu, S. (2022) Jointly Modeling Transfer Learning of Industrial Chain Information and Deep Learning for Stock Prediction. Expert Systems with Applications, 191, Article ID: 116257. https://doi.org/10.1016/j.eswa.2021.116257
[18]
Azevedo, A. and Santos, M.F. (2008) KDD, SEMMA and CRISP-DM: A Parallel Overview. IADIS European Conference Data Mining, Amsterdam, 24-26 July 2008, 182-185.
[19]
Rathore, V.S., Worring, M., Mishra, D.K., Joshi, A. and Maheshwari, S. (2018) Emerging Trends in Expert Applications and Security: Proceedings of ICETEAS 2018. Springer, Berlin, 485-495. https://doi.org/10.1007/978-981-13-2285-3
[20]
Huynh, H.D., Dang, L.M. and Duong, D. (2017) A New Model for Stock Price Movements Prediction Using Deep Neural Network. Proceedings of the 8th International Symposium on Information and Communication Technology, Nha Trang, 7-8 December 2017, 57-62. https://doi.org/10.1145/3155133.3155202
[21]
Hochreiter, S. and Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
[22]
Farahani, A., Pourshojae, B., Rasheed, K. and Arabnia, H.R. (2020) A Concise Review of Transfer Learning. Proceedings 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, Las Vegas, 16-18 December 2020, 344-351. https://doi.org/10.1109/CSCI51800.2020.00065
[23]
Ubert de Almeida, B., Ferreira Neves, R. and Horta, N. (2018) Combining Support Vector Machine with Genetic Algorithms to Optimize Investments in Forex Markets with High Leverage. Applied Soft Computing Journal, 64, 596-613. https://doi.org/10.1016/j.asoc.2017.12.047
