Affiliation:
1. Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht 41938-1914, Iran
Abstract
Cryptocurrency prediction is important for a variety of stakeholders, from investors to businesses, as it enables them to make more informed decisions about the future of the digital asset market. This paper delves into the application of deep learning models for two of the most popular cryptocurrencies, Bitcoin and Ethereum, outlining how to effectively implement these methods. Our goal is to perform efficient deep learning structure based on the forecasting models specifically recurrent neural networks, convolutional neural network and long short-term memory to predict the Bitcoin and Ethereum prices. Our results include a comparison of these two cryptocurrencies according to the deep learning methods and their effectiveness in predicting the Bitcoin and Ethereum prices.
Publisher
World Scientific Pub Co Pte Ltd
Subject
Materials Science (miscellaneous)
Cited by
1 articles.
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1. Ethereum Cryptocurrency Prediction using ML procedures on Recurrent Neural Network using LSTM Model;2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON);2023-12-29