Abstract
In the volatile world of cryptocurrency markets, accurate price prediction is crucial for investors and businessesfoundation for research, analysis and modelling. This paper introduces an innovative approach using deep learning techniques to enhance cryptocurrency price prediction. Focusing on 10 prominent cryptocurrencies, we employ LSTM and GRU models to improve prediction accuracy. We preprocess data using Min-Max scaling, optimizing model convergence. Furthermore, we compare the performance of each cryptocurrency with these models to determine which ones are better suited for specific deep learning models. By integrating these techniques, our framework not only outperforms traditional methods but also offers insights into which cryptocurrencies are more compatible with particular models. This research contributes to the advancement of cryptocurrency prediction and aids better financial decision-making.
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Ponselvakumar, A.P., Shankar, V.P.G., Iniyan, G., Logesh, B. (2024). Improving the Cryptocurrency Price Prediction Using Deep Learning. In: Abraham, A., Pllana, S., Hanne, T., Siarry, P. (eds) Intelligent Systems Design and Applications. ISDA 2023. Lecture Notes in Networks and Systems, vol 1048. Springer, Cham. https://doi.org/10.1007/978-3-031-64650-8_14
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