Predictive Analysis of Cryptocurrency Price Using Deep Learning

Author:

Yao Yecheng,Yi Jungho,Zhai Shengjun,Lin Yuwen,Kim Taekseung,Zhang Guihongxuan,Yoonjae Lee Leonard

Abstract

The decentralization of cryptocurrencies has greatly reduced the level of central control over them, impacting international relations and trade. Further, wide fluctuations in cryptocurrency price indicate an urgent need for an accurate way to forecast this price. This paper proposes a novel method to predict cryptocurrency price by considering various factors such as market cap, volume, circulating supply, and maximum supply based on deep learning techniques such as the recurrent neural network (RNN) and the long short-term memory (LSTM),which are effective learning models for training data, with the LSTM being better at recognizing longer-term associations. The proposed approach is implemented in Python and validated for benchmark datasets. The results verify the applicability of the proposed approach for the accurate prediction of cryptocurrency price.

Publisher

Science Publishing Corporation

Subject

Hardware and Architecture,General Engineering,General Chemical Engineering,Environmental Engineering,Computer Science (miscellaneous),Biotechnology

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2. Candlestick Pattern Recognition in Cryptocurrency Price Time-Series Data Using Rule-Based Data Analysis Methods;Computation;2024-06-29

3. Predictive Analysis of Bitcoin Prices Using Bidirectional Long Short-Term Memory Networks;2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO);2024-06-14

4. A Study on Hybrid Deep Learning Approaches for “Monero” Cryptocurrency Price Prediction;2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP);2024-02-21

5. LSTM-Based Dynamic Linguistic Decision-Making for Cryptocurrency Selection;Lecture Notes in Networks and Systems;2024

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