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Financial Technology and Innovation
Volume 2 , Issue 2, PP: 18-26 , 2023 | Cite this article as | XML | Html |PDF

Title

Time Series Forecasting of Cryptocurrency Prices with Long Short-Term Memory Networks

  El-Sayed M. El-Kenawy 1 * ,   Abdelaziz A. Abdelhamid 2 ,   Abdelhameed Ibrahim 3 ,   Marwa M. Eid 4 ,   Faris H. Rizk 5 ,   Ahmed Mohamed Zaki 6

1  Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
    (skenawy@ieee.org)

2  Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
    (abdelaziz@cis.asu.edu.eg)

3  Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
    (afai79@mans.edu.eg)

4  Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt; Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
    (mmm@ieee.org)

5  Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
    (faris.rizk@jcsis.org)

6  Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
    ( azaki@jcsis.org)

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Doi   :   https://doi.org/10.54216/FinTech-I.020202

Received: January 28, 2023 Accepted: June 22, 2023

Abstract :

The rapid evolution of cryptocurrencies has brought transformative changes to the financial landscape. Cryptocurrency prices, characterized by their inherent volatility, pose challenges for precise forecasting. This study introduces a novel approach to cryptocurrency price forecasting, leveraging Long Short-Term Memory (LSTM) networks, known for discerning temporal dependencies within time series data. Motivated to enhance prediction accuracy, this research investigates the effectiveness of LSTM networks in capturing complexities inherent in cryptocurrency price movements. The proposed methodology involves meticulous data collection and preprocessing, utilizing an extensive dataset from Kaggle. This dataset forms the foundation for predictive modeling and facilitates an in-depth analysis of cryptocurrency price dynamics. Exploratory data analysis, including visualization techniques, and a dedicated Time Series Analysis precede the implementation of predictive models, such as LSTM networks. Results and evaluation showcase promising outcomes, emphasizing the models' precision, accuracy, and explanatory power. The Mean Absolute Error (MAE) of 0.0177 underscores the precision achieved in predicting cryptocurrency prices, while the Mean Squared Error (MSE) of 0.00066 and the R² Score of 0.9486 attest to our models' overall accuracy and explanatory power. This research significantly contributes to understanding cryptocurrency forecasting by incorporating LSTM networks, paving the way for advancements in this evolving domain.

Keywords :

Cryptocurrency; Long Short-Term Memory (LSTM) networks; Price Forecasting; Time Series Analysis.

References :

[1]     Beck, R., Avital, M., Rossi, M., & Thatcher, J. B. (2017). Blockchain Technology in Business and Information Systems Research. Business & Information Systems Engineering, 59(6), 381–384. https://doi.org/10.1007/s12599-017-0505-1

[2]     Abu-elezz, I., Hassan, A., Nazeemudeen, A., Househ, M., & Abd-alrazaq, A. (2020). The benefits and threats of blockchain technology in healthcare: A scoping review. International Journal of Medical Informatics, 142, 104246. https://doi.org/10.1016/j.ijmedinf.2020.104246

[3]     Djaafari, A., Ibrahim, A., Bailek, N., Bouchouicha, K., Hassan, M. A., Kuriqi, A., Al-Ansari, N., & El-kenawy, E.-S. M. (2022). Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions. Energy Reports, 8, 15548–15562. https://doi.org/10.1016/j.egyr.2022.10.402

[4]     Hasankhani, A., Mehdi Hakimi, S., Bisheh-Niasar, M., Shafie-khah, M., & Asadolahi, H. (2021). Blockchain technology in the future smart grids: A comprehensive review and frameworks. International Journal of Electrical Power & Energy Systems, 129, 106811. https://doi.org/10.1016/j.ijepes.2021.106811

[5]     Khafaga, D. S., Ibrahim, A., El-Kenawy, E.-S. M., Abdelhamid, A. A., Karim, F. K., Mirjalili, S., Khodadadi, N., Lim, W. H., Eid, M. M., & Ghoneim, M. E. (2022). An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease. Diagnostics, 12(11), Article 11. https://doi.org/10.3390/diagnostics12112892

[6]     Patel, M. M., Tanwar, S., Gupta, R., & Kumar, N. (2020). A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions. Journal of Information Security and Applications, 55, 102583. https://doi.org/10.1016/j.jisa.2020.102583

[7]     Akyildirim, E., Goncu, A., & Sensoy, A. (2021). Prediction of cryptocurrency returns using machine learning. Annals of Operations Research, 297(1), 3–36. https://doi.org/10.1007/s10479-020-03575-y

[8]     M., P., Nguyen, T. N., Hamdi, M., & Cengiz, K. (2021). Global cryptocurrency trend prediction using social media. Information Processing & Management, 58(6), 102708. https://doi.org/10.1016/j.ipm.2021.102708

[9]     Huang, X., Zhang, W., Tang, X., Zhang, M., Surbiryala, J., Iosifidis, V., Liu, Z., & Zhang, J. (2021). LSTM Based Sentiment Analysis for Cryptocurrency Prediction. In C. S. Jensen, E.-P. Lim, D.-N. Yang, W.-C. Lee, V. S. Tseng, V. Kalogeraki, J.-W. Huang, & C.-Y. Shen (Eds.), Database Systems for Advanced Applications (pp. 617–621). Springer International Publishing. https://doi.org/10.1007/978-3-030-73200-4_47

[10] Chowdhury, R., Rahman, M. A., Rahman, M. S., & Mahdy, M. R. C. (2020). An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning. Physica A: Statistical Mechanics and Its Applications, 551, 124569. https://doi.org/10.1016/j.physa.2020.124569

[11] M., P., Sharma, A., V., V., Bhardwaj, V., Sharma, A. P., Iqbal, R., & Kumar, R. (2020). Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system. Computers & Electrical Engineering, 81, 106527. https://doi.org/10.1016/j.compeleceng.2019.106527

[12] Lahmiri, S., & Bekiros, S. (2019). Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos, Solitons & Fractals, 118, 35–40. https://doi.org/10.1016/j.chaos.2018.11.014

[13] Nasir, M. A., Huynh, T. L. D., Nguyen, S. P., & Duong, D. (2019). Forecasting cryptocurrency returns and volume using search engines. Financial Innovation, 5(1), 2. https://doi.org/10.1186/s40854-018-0119-8

[14] Sun, X., Liu, M., & Sima, Z. (2020). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32, 101084. https://doi.org/10.1016/j.frl.2018.12.032

[15] Zhang, Z., Dai, H.-N., Zhou, J., Mondal, S. K., García, M. M., & Wang, H. (2021). Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels. Expert Systems with Applications, 183, 115378. https://doi.org/10.1016/j.eswa.2021.115378

[16] Cryptocurrency Prediction Artificial Intelligence. (n.d.). [dataset]. Retrieved December 13, 2023, from https://www.kaggle.com/datasets/emirhanai/cryptocurrency-prediction-artificial-intelligence/data

[17] Samee, N., El-Kenawy, E.-S., Atteia, G., Jamjoom, M., Ibrahim, A., Abdelhamid, A., El-Attar, N., Gaber, T., Slowik, A., & Shams, M. (2022). Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images. Computers, Materials & Continua, 73(2), 4193–4210. https://doi.org/10.32604/cmc.2022.031147

[18] Calle, M. L., Pujolassos, M., & Susin, A. (2023). coda4microbiome: Compositional data analysis for microbiome cross-sectional and longitudinal studies. BMC Bioinformatics, 24(1), 82. https://doi.org/10.1186/s12859-023-05205-3

[19] Kristensen, S. B., Clausen, A., Skjødt, M. K., Søndergaard, J., Abrahamsen, B., Möller, S., & Rubin, K. H. (2023). An enhanced version of FREM (Fracture Risk Evaluation Model) using national administrative health data: Analysis protocol for development and validation of a multivariable prediction model. Diagnostic and Prognostic Research, 7(1), 19. https://doi.org/10.1186/s41512-023-00158-w

[20] Sarker, I. H. (2023). Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects. Annals of Data Science, 10(6), 1473–1498. https://doi.org/10.1007/s40745-022-00444-2

[21] Eid, M. M., El-Kenawy, E.-S. M., Khodadadi, N., Mirjalili, S., Khodadadi, E., Abotaleb, M., Alharbi, A. H., Abdelhamid, A. A., Ibrahim, A., Amer, G. M., Kadi, A., & Khafaga, D. S. (2022). Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases. Mathematics, 10(20), Article 20. https://doi.org/10.3390/math10203845

[22] Xu, X., Wei, A., Tang, S., Liu, Q., Shi, H., & Sun, W. (2023). Prediction of nitrous oxide emission of a municipal wastewater treatment plant using LSTM-based deep learning models. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-023-31250-9

Cite this Article as :
Style #
MLA El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, Faris H. Rizk, Ahmed Mohamed Zaki. "Time Series Forecasting of Cryptocurrency Prices with Long Short-Term Memory Networks." Financial Technology and Innovation, Vol. 2, No. 2, 2023 ,PP. 18-26 (Doi   :  https://doi.org/10.54216/FinTech-I.020202)
APA El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, Faris H. Rizk, Ahmed Mohamed Zaki. (2023). Time Series Forecasting of Cryptocurrency Prices with Long Short-Term Memory Networks. Journal of Financial Technology and Innovation, 2 ( 2 ), 18-26 (Doi   :  https://doi.org/10.54216/FinTech-I.020202)
Chicago El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, Faris H. Rizk, Ahmed Mohamed Zaki. "Time Series Forecasting of Cryptocurrency Prices with Long Short-Term Memory Networks." Journal of Financial Technology and Innovation, 2 no. 2 (2023): 18-26 (Doi   :  https://doi.org/10.54216/FinTech-I.020202)
Harvard El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, Faris H. Rizk, Ahmed Mohamed Zaki. (2023). Time Series Forecasting of Cryptocurrency Prices with Long Short-Term Memory Networks. Journal of Financial Technology and Innovation, 2 ( 2 ), 18-26 (Doi   :  https://doi.org/10.54216/FinTech-I.020202)
Vancouver El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, Faris H. Rizk, Ahmed Mohamed Zaki. Time Series Forecasting of Cryptocurrency Prices with Long Short-Term Memory Networks. Journal of Financial Technology and Innovation, (2023); 2 ( 2 ): 18-26 (Doi   :  https://doi.org/10.54216/FinTech-I.020202)
IEEE El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, Faris H. Rizk, Ahmed Mohamed Zaki, Time Series Forecasting of Cryptocurrency Prices with Long Short-Term Memory Networks, Journal of Financial Technology and Innovation, Vol. 2 , No. 2 , (2023) : 18-26 (Doi   :  https://doi.org/10.54216/FinTech-I.020202)
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