Investment Decision on Cryptocurrency: Comparing Prediction Performance Using ARIMA and LSTM

  • Svend Pasak Bina Nusantara University, Indonesia
  • Riyanto Jayadi Bina Nusantara University, Indonesia
Keywords: Cryptocurrency, forecast, accuracy, ARIMA, LSTM


The increasing popularity of cryptocurrencies as a means of financial inclusion for investment and trade has become a major concern for individuals seeking to benefit from the cryptocurrency market. This study aims to provide insights for cryptocurrency investors, financial sector professionals, and academics by utilizing machine learning techniques such as ARIMA and LSTM to compare the accuracy of modeling performance on datasets predicting the prices of five cryptocurrencies, namely Bitcoin, Ethereum, Binance Coin, Tether, and Cardano. Data was obtained by downloading from the Yahoo Finance website using Jupyter notebook. The LSTM method outperformed the ARIMA method, achieving a lower MAPE value of less than 10 percent and effectively capturing price movements, providing valuable information for decision-making.


Download data is not yet available.


N. Gonzálvez-Gallego and M. C. Pérez-Cárceles, “Cryptocurrencies and illicit practices: The role of governance,” Econ. Anal. Policy, vol. 72, pp. 203–212, 2021, doi: 10.1016/j.eap.2021.08.003.

Ferdiansyah, S. H. Othman, R. Z. M. Radzi, D. Stiawan, and T. Sutikno, “Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy,” IAES Int. J. Artif. Intell., vol. 12, no. 1, pp. 251–261, 2023, doi: 10.11591/ijai.v12.i1.pp251-261.

M. Ortu, S. Vacca, G. Destefanis, and C. Conversano, “Cryptocurrency ecosystems and social media environments: An empirical analysis through Hawkes’ models and natural language processing,” Mach. Learn. with Appl., vol. 7, no. November 2021, p. 100229, 2022, doi: 10.1016/j.mlwa.2021.100229.

C. C. Whitlock, “Cryptocurrency scam warning: ‘Fomo’ leads to £63m being lost through social media fraud,” May 26, 2021.

M. Del Castillo and S. Ehrlich, “Crypto’s Great Reset: How Digital Asset Investors Will Recover From The Market’s $1 Trillion Meltdown,” May 2022.

A. Ugolini, J. C. Reboredo, and W. Mensi, “safe-haven assets,” vol. 53, no. November 2022, 2023.

A. A. Oyedele, A. O. Ajayi, L. O. Oyedele, S. A. Bello, and K. O. Jimoh, “Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction,” Expert Systems with Applications, vol. 213. 2023, doi: 10.1016/j.eswa.2022.119233.

R. Chowdhury, M. A. Rahman, M. S. Rahman, and M. R. C. Mahdy, “Predicting and forecasting the price of constituents and index of cryptocurrency using machine learning,” arXiv, pp. 1–38, 2019.

S. Alonso-Monsalve, A. L. Suárez-Cetrulo, A. Cervantes, and D. Quintana, “Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators,” Expert Syst. Appl., vol. 149, p. 113250, 2020, doi: 10.1016/j.eswa.2020.113250.

M. M. Patel, S. Tanwar, R. Gupta, and N. Kumar, “A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions,” J. Inf. Secur. Appl., vol. 55, no. June, 2020, doi: 10.1016/j.jisa.2020.102583.

M. Poongodi et al., “Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system,” Comput. Electr. Eng., vol. 81, p. 106527, 2020, doi: 10.1016/j.compeleceng.2019.106527.

S. A. David, C. M. C. Inacio, R. Nunes, and J. A. T. Machado, “Fractional and fractal processes applied to cryptocurrencies price series,” J. Adv. Res., vol. 32, pp. 85–98, 2021, doi: 10.1016/j.jare.2020.12.012.

N. A. Hitam, A. R. Ismail, and F. Saeed, “An Optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) for Cryptocurrency Forecasting,” Procedia Comput. Sci., vol. 163, pp. 427–433, 2019, doi: 10.1016/j.procs.2019.12.125.

T. Zoumpekas, E. Houstis, and M. Vavalis, “ETH analysis and predictions utilizing deep learning,” Expert Syst. Appl., vol. 162, no. March, p. 113866, 2020, doi: 10.1016/j.eswa.2020.113866.

S. Lahmiri and S. Bekiros, “Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market,” Chaos, Solitons and Fractals, vol. 133, 2020, doi: 10.1016/j.chaos.2020.109641.

T. A. Borges and R. F. Neves, “Ensemble of machine learning algorithms for cryptocurrency investment with different data resampling methods,” Appl. Soft Comput. J., vol. 90, p. 106187, 2020, doi: 10.1016/j.asoc.2020.106187.

Z. Chen, C. Li, and W. Sun, “Bitcoin price prediction using machine learning: An approach to sample dimension engineering,” J. Comput. Appl. Math., vol. 365, p. 112395, 2020, doi: 10.1016/

J. Sun, “Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models,” Comput. Methods Programs Biomed. Updat., vol. 1, no. April, p. 100029, 2021, doi: 10.1016/j.cmpbup.2021.100029.

Q. Yang, J. Wang, H. Ma, and X. Wang, “Research on COVID-19 based on ARIMA modelΔ—Taking Hubei, China as an example to see the epidemic in Italy,” J. Infect. Public Health, vol. 13, no. 10, pp. 1415–1418, 2020, doi: 10.1016/j.jiph.2020.06.019.

O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, “Financial time series forecasting with deep learning: A systematic literature review: 2005–2019,” Appl. Soft Comput. J., vol. 90, p. 106181, 2020, doi: 10.1016/j.asoc.2020.106181.

D. Kobiela, D. Krefta, W. Król, and P. Weichbroth, “ARIMA vs LSTM on NASDAQ stock exchange data,” Procedia Computer Science, vol. 207. pp. 3830–3839, 2022, doi: 10.1016/j.procs.2022.09.445.

A. Yadav, C. K. Jha, and A. Sharan, “Optimizing LSTM for time series prediction in Indian stock market,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 2091–2100, 2020, doi: 10.1016/j.procs.2020.03.257.

J. Zhang, Y. Zeng, and B. Starly, “Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis,” no. February, 2021, doi:

G. Ardiyansyah, Ferdiansyah;, and U. Ependi, “View of Deep Learning Model Analysis and Web-Based Implementation of Cryptocurrency Prediction,” J. Inf. Syst. Informatics, vol. 4, 2022, doi: 10.51519/journalisi.v4i4.365.

S. Mirzaei, J. L. Kang, and K. Y. Chu, “A comparative study on long short-term memory and gated recurrent unit neural networks in fault diagnosis for chemical processes using visualization,” J. Taiwan Inst. Chem. Eng., vol. 130, 2022, doi: 10.1016/j.jtice.2021.08.016.

Abstract views: 73 times
Download PDF: 57 times
How to Cite
Pasak, S., & Jayadi, R. (2023). Investment Decision on Cryptocurrency: Comparing Prediction Performance Using ARIMA and LSTM. Journal of Information Systems and Informatics, 5(2), 407-427.