Comparison of Deep Learning algorithms for cryptocurrencies price forecasting
Article Sidebar
Main Article Content
Due to the growth and interest that cryptocurrencies have generated nowadays, investors and researchers have paid more attention to forecasting the prices of cryptocurrencies. With the methodological development of Deep Learning, forecasting cryptocurrencies has become more important in recent years. In this paper, four Deep Learning models RNN, LSTM, GRU and CNN-LSTM are evaluated with the aim of evaluating the performance in forecasting the daily closing price of the two most important cryptocurrencies: Bitcoin and Ethereum. Performance analysis metrics such as MAE, RMSE, MSE and MAPE were used and as a fitting metric, the R2. Each Deep Learning model was optimized from a set of hyperparameters and for different time windows. Experimental results showed that the RNN algorithm had superior performance in predicting the Bitcoin price and the LSTM algorithm in predicting the Ethereum price. Even, both methods presented better performance with two literature models evaluated. Finally, the forecast reliability of each model was evaluated by analyzing the autocorrelation of the errors and the two most efficient models were found to have high generalization power.
Kang CY, Lee CP, Lim KM. Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit. Data. 2022;7(11). https://doi.org/10.3390/data7110149 DOI: https://doi.org/10.3390/data7110149
Fleischer JP, von Laszewski G, Theran C, Bautista YJP. Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory. Algorithms. 2022;15(7). https://doi.org/10.3390/a15070230 DOI: https://doi.org/10.3390/a15070230
Ammer MA, Aldhyani THH. Deep Learning Algorithm to Predict Cryptocurrency Fluctuation Prices: Increasing Investment Awareness. Electron. 2022;11(15). https://doi.org/10.3390/electronics11152349 DOI: https://doi.org/10.3390/electronics11152349
Patel MM, Tanwar S, Gupta R, Kumar N. A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions. J Inf Secur Appl. 2020;55. https://doi.org/10.1016/j.jisa.2020.102583 DOI: https://doi.org/10.1016/j.jisa.2020.102583
Lahmiri S, Bekiros S. Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos, Solitons and Fractals. 2019;118:35-40. https://doi.org/10.1016/j.chaos.2018.11.014 DOI: https://doi.org/10.1016/j.chaos.2018.11.014
Wu C-H, Lu C-C, Ma Y-F, Lu R-S. A new forecasting framework for bitcoin price with LSTM. In: IEEE International Conference on Data Mining Workshops, ICDMW. 2019. p. 168-75.https://doi.org/10.1109/ICDMW.2018.00032 DOI: https://doi.org/10.1109/ICDMW.2018.00032
Pintelas E, Livieris IE, Stavroyiannis S, Kotsilieris T, Pintelas P. Investigating the Problem of Cryptocurrency Price Prediction: A Deep Learning Approach. Vol. 584 IFIP, IFIP Advances in Information and Communication Technology. 2020. 99-110 p. https://doi.org/10.1007/978-3-030-49186-4_9 DOI: https://doi.org/10.1007/978-3-030-49186-4_9
Awoke T, Rout M, Mohanty L, Satapathy SC. Bitcoin Price Prediction and Analysis Using Deep Learning Models. Vol. 134, Lecture Notes in Networks and Systems. 2021. 631-640 p. https://doi.org/10.1007/978-981-15-5397-4_63 DOI: https://doi.org/10.1007/978-981-15-5397-4_63
Oyedele AA, Ajayi AO, Oyedele LO, Bello SA, Jimoh KO. Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction. Expert Syst Appl. 2023;213. https://doi.org/10.1016/j.eswa.2022.119233 DOI: https://doi.org/10.1016/j.eswa.2022.119233
Ferdiansyah, Othman SH, Radzi RZM, Stiawan D, Sutikno T. Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy. IAES Int J Artif Intell. 2023;12(1):251-61. https://doi.org/10.11591/ijai.v12.i1.pp251-261 DOI: https://doi.org/10.11591/ijai.v12.i1.pp251-261
Nasirtafreshi I. Forecasting cryptocurrency prices using Recurrent Neural Network and Long Short-term Memory. Data Knowl Eng. 2022;139. https://doi.org/10.1016/j.datak.2022.102009 DOI: https://doi.org/10.1016/j.datak.2022.102009
Bao W, Yue J, Rao Y. A deep learning framework for financial time series using stacked autoencoders and longshort term memory. PLoS One. 2017;12(7):1-24. https://doi.org/10.1371/journal.pone.0180944 DOI: https://doi.org/10.1371/journal.pone.0180944
Kim HY, Won CH. Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst Appl [Internet]. 2018;103:25-37. Available from: https://doi.org/10.1016/j.eswa.2018.03.002 DOI: https://doi.org/10.1016/j.eswa.2018.03.002
Zha W, Liu Y, Wan Y, Luo R, Li D, Yang S, et al. Forecasting monthly gas field production based on the CNN-LSTM model. Energy [Internet]. 2022;260(August):124889. Available from: https://doi.org/10.1016/j.energy.2022.124889 DOI: https://doi.org/10.1016/j.energy.2022.124889
Luo H, Wang D, Cheng J, Wu Q. Multi-step-ahead copper price forecasting using a two-phase architecture based on an improved LSTM with novel input strategy and error correction. Resour Policy. 2022;79(August). https://doi.org/10.1016/j.resourpol.2022.102962 DOI: https://doi.org/10.1016/j.resourpol.2022.102962
Lawi A, Mesra H, Amir S. Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately. J Big Data [Internet]. 2022;9(1). Available from: https://doi.org/10.1186/s40537-022-00597-0 DOI: https://doi.org/10.1186/s40537-022-00597-0
Tang H, Ling X, Li L, Xiong L, Yao Y, Huang X. One-shot pruning of gated recurrent unit neural network by sensitivity for time-series prediction. Neurocomputing [Internet]. 2022;512:15-24. Available from: https://doi.org/10.1016/j.neucom.2022.09.026 DOI: https://doi.org/10.1016/j.neucom.2022.09.026
Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, et al. Recent advances in convolutional neural networks. Pattern Recognit [Internet]. 2018;77:354-77. Available from: https://doi.org/10.1016/j.patcog.2017.10.013 DOI: https://doi.org/10.1016/j.patcog.2017.10.013
Ghimire S, Deo RC, Casillas-Pérez D, Salcedo-Sanz S, Sharma E, Ali M. Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction. Meas J Int Meas Confed [Internet]. 2022;202(August):111759. Available from: https://doi.org/10.1016/j.measurement.2022.111759 DOI: https://doi.org/10.1016/j.measurement.2022.111759
Livieris IE, Stavroyiannis S, Pintelas E, Pintelas P. A novel validation framework to enhance deep learning models in time-series forecasting. Neural Comput Appl. 2020;32(23):17149-67. https://doi.org/10.1007/s00521-020-05169-y DOI: https://doi.org/10.1007/s00521-020-05169-y
Accepted 2023-09-18
Published 2023-06-26
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors grant the journal and Universidad del Valle the economic rights over accepted manuscripts, but may make any reuse they deem appropriate for professional, educational, academic or scientific reasons, in accordance with the terms of the license granted by the journal to all its articles.
Articles will be published under the Creative Commons 4.0 BY-NC-SA licence (Attribution-NonCommercial-ShareAlike).