Currency movement forecasting using time series analysis and long short-term memory
Keywords:Foreign exchange, Forecasting, LSTM, ARIMA
AbstractForeign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day.
Czekalski, P., Niezabitowski, M., & Styblinski, R. (2015). ANN for FOREX forecasting and trading. Proceedings - 2015 20th International Conference on Control Systems and Computer
Science, CSCS 2015, 322â€“328. https://doi.org/10.1109/CSCS.2015.51
Diebold, F. X., & Kilian, L. (2000). Unit-root tests are useful for selecting forecasting models. Journal of Business and Economic Statistics, 18, 265-273.
Goyal, P., Pandey, S., & Jain, K. (2018). Deep learning for natural language processing. New York: Apress.
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 26(3).
Konar, A., & Bhattacharya, D. (2017). Time-series prediction and applications. In Intelligent systems reference library (Vol. 127, pp. 1868â€“4394).
Korczak, J., & Hemes, M. (2017). Deep learning for financial time series forecasting in A-Trader system. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, 905â€“912. https://doi.org/10.15439/2017F449
Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature 521(7553):436-444
Manaswi, N. K. (2018). Deep learning with applications using python. New York: Apress.
Michelucci, U. (2018). Applied deep learning: A case-based approach to understanding deep neural networks. New York: Apress.
Ming, Y., Cao, S., Zhang, R., Li, Z., Chen, Y., Song, Y., & Qu, H. (2017). Understanding hidden memories of recurrent neural networks. 2017 IEEE Conference on Visual Analytics Science
and Technology, VAST 2017, (October 2017), 17. https://doi.org/10.1109/VAST.2017.8585721
Moore, M. J., & Roche, M. J. (2015). Less of puzzle: A new look at the forward forex market. Journal of International Economics, 58(2), 387â€“411.
Nagpure, A. R. (2019). Prediction of multi-currency exchange rates using deep learning. International Journal of Innovative Technology and Exploring Engineering, 8(6), 316â€“322.
Ni, L., Li, Y., Wang, X., Zhang, J., Yu, J., & Qi, C. (2019). Forecasting of Forex Time Series Data Based on Deep Learning. Procedia Computer Science, 147, 647â€“652. https://doi.org/10.1016/j.procs.2019.01.189
Nielsen, A. (2019). Practical time series analysis (1st ed.). Sebastopol: Oâ€™Reilly media Inc.
Ong, E. (2019). Technical analysis for mega profit. Jakarta: PT Gramedia pustaka utama.
Palma, W. (2016). Time series analysis. New Jersey: John Wiley & Sons, Inc.
Reddy SK, B. A. (2015). Exchange rate forecasting using ARIMA, neural network and fuzzy neuron. Journal of Stock & Forex Trading, 04(03). https://doi.org/10.4172/2168-
Rigters, G. (2019). Forex trading for beginners & dummies. Retrieved from
Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005â€“2019. Applied Soft Computing Journal,
90, 106181. https://doi.org/10.1016/j.asoc.2020.106181
Song, X., Liu, Y., Xue, L., Wang, J., Zhang, J., Wang, J., â€¦ Cheng, Z. (2020). Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model.
Journal of Petroleum Science and Engineering, 186(July 2019), 106682. https://doi.org/10.1016/j.petrol.2019.106682
Wang, X., Smith, K., & Hyndman, R. (2006). Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery, 13(3), 335-364.
Yang, Q., Wang, J., Ma, H., & Wang, X. (2020). Research on COVID-19 Based on ARIMA ModelÎ”â€”Taking Hubei, China as an example to see the epidemic in Italy. Journal of
Infection and Public Health, 4â€“7. https://doi.org/10.1016/j.jiph.2020.06.019
Yong, Y. L., Lee, Y., Gu, X., Angelov, P. P., Ngo, D. C. L., & Shafipour, E. (2018). Foreign currency exchange rate prediction using neuro-fuzzy systems. Procedia Computer Science, 144,
Zheng, Y., Liu, Q., and Chen, E., (2014), Time series classification using multi-channels deep convolutional neural networks. Web-Age Information Management. Springer International
Publishing 2014: 201-210
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