Abstract
The cryptocurrency market is a fast-growing and highly volatile market that poses significant challenges for investors seeking to make informed decisions. As the market continues to evolve and become more mainstream, there is a growing need for accurate forecasting methods to help investors navigate this unpredictable terrain. This study examines the potential of machine learning techniques, specifically Long Short-Term Memory (LSTM), for predicting future trends in the cryptocurrency market. The study evaluates the effectiveness of various machine learning algorithms in predicting the price movements of different cryptocurrency assets. The research employs historical data analysis and machine learning techniques, focusing on LSTM. LSTM is a neural network that can capture long-term dependencies in time-series data suited for predicting trends in the cryptocurrency market. The study uses performance criteria such as recall precision, measuring how well an algorithm can identify true-positive and negative cases in a dataset, required for assessing its accuracy and effectiveness. This study has the potential to provide valuable insights into the effectiveness of machine learning techniques, specifically LSTM, for predicting trends in the cryptocurrency market. Investors can make better decisions in this highly volatile and unpredictable market by identifying the most effective and efficient algorithm expanding the use of machine learning in the financial forecasting domain.
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References
White, L.H.: The market for cryptocurrencies. SSRN Electron. J. (2014). https://doi.org/10.2139/ssrn.2538290
Gupta, H., Chaudhary, R.: An empirical study of volatility in cryptocurrency market. J. Risk Financ. Manage. 15(11), 513 (2022). https://doi.org/10.3390/jrfm15110513
Almeida, J., Gonçalves, T.C.: A systematic literature review of investor behavior in the cryptocurrency markets. J. Behav. Exp. Financ. 37, 100785 (2023). https://doi.org/10.1016/j.jbef.2022.100785
Gurupradeep, G., Harishvaran, M., Amsavalli, K.: Cryptocurrency price prediction using machine learning. IJARCCE 12 (2023). https://doi.org/10.17148/IJARCCE.2023.124140
Song, X., et al.: Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. J. Petrol. Sci. Eng. 186, 106682 (2020). https://doi.org/10.1016/j.petrol.2019.106682
Zhou, L., Pan, S., Wang, J., Vasilakos, A.V.: Machine learning on big data: opportunities and challenges. Neurocomputing 237, 350–361 (2017). https://doi.org/10.1016/j.neucom.2017.01.026
Sarker, I.H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2(3) (2021). https://doi.org/10.1007/s42979-021-00592-x
Prasad, A., Seetharaman, A.: Importance of machine learning in making investment decision in stock market. Vikalpa J. Decis. Mak. 46(4), 209–222 (2021). https://doi.org/10.1177/02560909211059992
Salman, K., Ibrahim, A.: Price prediction of different cryptocurrencies using technical trade indicators and machine learning. IOP Conf. Ser. Mater. Sci. Eng. 928(3), 032007 (2020). https://doi.org/10.1088/1757899X/928/3/032007
Lindemann, B., Müller, T., Vietz, H., Jazdi, N., Weyrich, M.: A survey on long shortterm memory networks for time series prediction. In: 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 15–17 July 2020, vol. 99, pp. 650–655 (2021). https://doi.org/10.1016/j.procir.2021.03.088
Zhang, S., Li, M., Yan, C.: The empirical analysis of bitcoin price prediction based on deep learning integration method. Comput. Intell. Neurosci. 2022, 1–9 (2022). https://doi.org/10.1155/2022/1265837
Amirshahi, B., Lahmiri, S.: Hybrid deep learning and GARCHfamily models for forecasting volatility of cryptocurrencies. Mach. Learn. Appl. 12, 100465 (2023). https://doi.org/10.1016/j.mlwa.2023.100465
Palakurla, S.: Predictive analysis of cryptocurrency using machine learning with blockchain technology. Machine Learning, December 2020
Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., De Felice, F.: Artificial intelligence and machine learning applications in smart production: progress, trends, and directions. Sustainability 12(2), 492 (2020). https://doi.org/10.3390/su12020492
Sebastião, H., Godinho, P.: Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financ. Innov. 7(1) (2021). https://doi.org/10.1186/s40854-020-00217-x
Bolt, W.: Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction (2017)
Park, S., Yang, J.-S.: Interpretable deep learning LSTM model for intelligent economic decision-making. Knowl. Based Syst. 248, 108907 (2022). https://doi.org/10.1016/j.knosys.2022.108907
Jeyakumar, S., Hou, Z., Yugarajah, A., Palaniswami, M., Muthukkumarasamy, V.: Visualizing Blockchain Transaction Behavioural Pattern: A Graphbased Approach (2023). https://doi.org/10.36227/techrxiv.22329889.v1
Regev, Y., Vassdal, H., Halden, U., Catak, F.O., Cali, U.: Hybrid AIbased anomaly detection model using Phasor measurement unit data (2022). https://doi.org/10.48550/arXiv.2209.12665
Zetzsche, D.A., Arner, D.W., Buckley, R.P.: Decentralized finance. J. Financ. Regul. 6(2), 172–203 (2020). https://doi.org/10.1093/jfr/fjaa010
Chen, J.: Analysis of bitcoin price prediction using machine learning. J. Risk Financ. Manage. 16(1), 51 (2023). https://doi.org/10.3390/jrfm16010051
Liu, D., Wei, A.: Regulated LSTM artificial neural networks for option risks. FinTech 1(2), 180–190 (2022). https://doi.org/10.3390/fintech1020014
Liemohn, M.W., Shane, A.D., Azari, A.R., Petersen, A.K., Swiger, B.M., Mukhopadhyay, A.: RMSE is not enough: guidelines to robust datamodel comparisons for magnetospheric physics. J. Atmos. Sol. Terr. Phys. 218, 105624 (2021). https://doi.org/10.1016/j.jastp.2021.105624
Huang, X., et al.: LSTM based sentiment analysis for cryptocurrency prediction, March 2021. https://doi.org/10.48550/arxiv.2103.14804
Livieris, I.E., Kiriakidou, N., Stavroyiannis, S., Pintelas, P.: An advanced CNN-LSTM model for cryptocurrency forecasting. Electronics 10(3), 287 (2021). https://doi.org/10.3390/electronics10030287
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Joshi, S., Satya, M., Domb, M. (2024). AI Based Commercial Decisions: The Cryptocurrency Market Case. In: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networks and Systems, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-031-62269-4_15
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