Cryptocurrency Price Prediction Deep Learning

Abstract

Cryptocurrencies are changing how we view and interact with traditional currencies, and they have become a disruptive force in the financial industry. Accurate price prediction is becoming more and more important as the bitcoin industry grows in size and complexity. This paper provides a thorough examination of deep learning models used in bitcoin price prediction. We explore the dynamic and unpredictable character of the cryptocurrency market, where price swings can happen quickly and without warning. To comprehend the present state of the art in this domain and pinpoint the shortcomings of the deep learning models in use today, we examine the body of existing literature. The data collecting and preprocessing methods used to get the bitcoin market data ready for modeling are described in the methodology section. Numerous deep learning models—Recurrent Neural Networks among them, Convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) networks are investigated. We go over hyperparameter tweaking, model training, and the assessment metrics that are used to gauge the performance of the model. We offer a thorough case study that focuses on forecasting the price of a particular cryptocurrency, like Bitcoin, in order to offer empirical insights. Our results provide light on the difficulties and possibilities involved in this project, emphasizing the need for creative solutions to address the market's uncertainties. We highlight the limitations and the necessity for additional research and innovation in the field of bitcoin price prediction as we outline the main lessons learned in the conclusion. We also discuss the consequences of using deep learning models to risk management in bitcoin investment and trading.