Improved Accuracy of Ethereum Exchange Rate Prediction Against USD Using CNN-LSTM Hybrid Model with Bayesian Optimization
DOI:
https://doi.org/10.33292/ijarlit.v3i1.40Abstract
This study evaluates the effectiveness of the CNN-LSTM hybrid model in predicting the Ethereum exchange rate against the United States Dollar (USD) by comparing the performance of the model without optimization and the model with hyperparameter optimization using Bayesian Optimization. The dataset used is sourced from Yahoo Finance covering the period 2017-2023. The results show that the CNN-LSTM model with hyperparameter optimization consistently outperforms the model without optimization, with improved prediction accuracy shown through the RMSE, MAE, MAPE, and R² values. Hyperparameter optimization resulted in an optimal configuration with 166 filters, kernel size 5, 168 LSTM units, 91 dense units, learning rate 0.00114, and batch size 32. This research confirms the effectiveness of the CNN-LSTM hybrid approach in predicting crypto exchange rates, and demonstrates the importance of hyperparameter optimization in improving prediction accuracy.References
C. Jin and Y. Li, "Cryptocurrency Price Prediction Using Frequency Decomposition and Deep Learning," Fractal Fract., vol. 7, no. 10, p. 708, 2023, doi: 10.3390/fractalfract7100708.
A. Aljadani, "DLCP2F: A DL-based Cryptocurrency Price Prediction Framework," Discov. Artif. Intell., vol. 2, no. 1, 2022, doi: 10.1007/s44163-022-00036-2.
S. Theiri, R. Nekhili, and J. Sultan, "Cryptocurrency Liquidity During the Russia-Ukraine War: The Case of Bitcoin and Ethereum," J. Risk Financ., vol. 24, no. 1, pp. 59-71, 2022, doi: 10.1108/jrf-05-2022-0103.
I. Makarov and A. Schoar, "Trading and Arbitrage in Cryptocurrency Markets," J. financ. econ., vol. 135, no. 2, pp. 293-319, 2020, doi: 10.1016/j.jfineco.2019.07.001.
R. Bruzg?, J. ?ernevi?ien?, A. Šapkauskien?, A. Ma?erinskien?, S. Masteika, and K. Driaunys, "Stylized Facts, Volatility Dynamics and Risk Measures of Cryptocurrencies," J. Bus. Econ. Manag., vol. 24, no. 3, pp. 527-550, 2023, doi: 10.3846/jbem.2023.19118.
V. Derbentsev, N. Datsenko, O. Stepanenko, and V. Bezkorovainyi, "Forecasting Cryptocurrency Prices Time Series Using Machine Learning Approach," SHS Web Conf., vol. 65, p. 2001, 2019, doi: 10.1051/shsconf/20196502001.
A. M. Khedr, I. Arif, P. R. P. V, M. El-Bannany, S. M. Alhashmi, and M. Sreedharan, "Cryptocurrency Price Prediction Using Traditional Statistical and Machine-learning Techniques: A Survey," Intell. Syst. Account. Financ. Manag., vol. 28, no. 1, pp. 3-34, 2021, doi: 10.1002/isaf.1488.
F. Valencia, A. Gómez-Espinosa, and B. Valdés-Aguirre, "Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning," Entropy, vol. 21, no. 6, p. 589, 2019, doi: 10.3390/e21060589.
H. Jang and J. Lee, "An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information," IEEE access, vol. 6, pp. 5427-5437, 2017.
S. McNally, J. Roche, and S. Caton, "Predicting the price of bitcoin using machine learning," in 2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP), 2018, pp. 339-343.
B. Tripathi and R. K. Sharma, "Modeling Bitcoin Prices Using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks," Comput. Econ., vol. 62, no. 4, pp. 1919-1945, 2022, doi: 10.1007/s10614-022-10325-8.
O. M. Ahmed, L. M. Haji, A. M. Ahmed, and N. M. Salih, "Bitcoin Price Prediction Using the Hybrid Convolutional Recurrent Model Architecture," Eng. Technol. Appl. Sci. Res., vol. 13, no. 5, pp. 11735-11738, 2023, doi: 10.48084/etasr.6223.
S. Ji, J. Kim, and H. Im, "A comparative study of bitcoin price prediction using deep learning," Mathematics, vol. 7, no. 10, p. 898, 2019.
T. Tran, L. V ??i, and L. B. Minh, "Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting," Adv. Technol. Innov., vol. 7, no. 4, pp. 258-269, 2022, doi: 10.46604/aiti.2022.9227.
H. Cho, Y.-J. Kim, E. Lee, D. Choi, Y. Lee, and W. Rhee, "Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks," Ieee Access, vol. 8, pp. 52588-52608, 2020, doi: 10.1109/access.2020.2981072.
M. A. Amou, K. Xia, S. Kamhi, and M. Mouhafid, "A Novel MRI Diagnosis Method for Brain Tumor Classification Based on CNN and Bayesian Optimization," Healthcare, vol. 10, no. 3, p. 494, 2022, doi: 10.3390/healthcare10030494.
E. Elgeldawi, A. Sayed, A. R. Galal, and A. M. Zaki, "Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis," Informatics, vol. 8, no. 4, p. 79, 2021, doi: 10.3390/informatics8040079.
H. Abbasimehr and R. Paki, "Prediction of COVID-19 Confirmed Cases Combining Deep Learning Methods and Bayesian Optimization," Chaos Solitons & Fractals, vol. 142, p. 110511, 2021, doi: 10.1016/j.chaos.2020.110511.
N. Bakhashwain and A. Sagheer, "Online Tuning of Hyperparameters in Deep LSTM for Time Series Applications," Int. J. Intell. Eng. Syst., vol. 14, no. 1, pp. 212-220, 2021, doi: 10.22266/ijies2021.0228.21.
P. Nickerson, R. Baharloo, A. Davoudi, A. Bihorac, and P. Rashidi, "Comparison of Gaussian Processes Methods to Linear Methods for Imputation of Sparse Physiological Time Series," pp. 4106-4109, 2018, doi: 10.1109/embc.2018.8513303.
K. Yan, X. Wang, Y. Du, N. Jin, H. Haichao, and H. Zhou, "Multi-Step Short-Term Power Consumption Forecasting With a Hybrid Deep Learning Strategy," Energies, vol. 11, no. 11, p. 3089, 2018, doi: 10.3390/en11113089.
W. A. Arifin, I. Ariawan, A. A. Rosalia, L. Lukman, and N. Tufailah, "Data Scaling Performance on Various Machine Learning Algorithms to Identify Abalone Sex," J. Technol. And System. Comput., vol. 10, no. 1, pp. 26-31, 2021, doi: 10.14710/jtsiskom.2021.14105.
M. M. Ahsan, M. A. P. Mahmud, P. Saha, K. D. Gupta, and Z. Siddique, "Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance," Technologies, vol. 9, no. 3, p. 52, 2021, doi: 10.3390/technologies9030052.
N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis, "Deep Adaptive Input Normalization for Time Series Forecasting," Ieee Trans. Neural Networks Learn. Syst., vol. 31, no. 9, pp. 3760-3765, 2020, doi: 10.1109/tnnls.2019.2944933.
Downloads
Published
Issue
Section
License
International Journal Artificial Intelligent and Informatics allows readers to read, download, copy, distribute, print, search, or link to its articles' full texts and allows readers to use them for any other lawful purpose. The journal allows the author(s) to hold the copyright without restrictions. Finally, the journal allows the author(s) to retain publishing rights without restrictions
- Authors are allowed to archive their submitted article in an open access repository
- Authors are allowed to archive the final published article in an open access repository with an acknowledgment of its initial publication in this journal
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 Generic License.