Ai Based Bitcoin Price Prediction Using Machine Learning

Abstract

Highly accurate predictions of cryptocurrency prices are of paramount importance to investors and researchers, as they can guide investment strategies and market analysis. However, due to the nonlinear and volatile nature of the cryptocurrency market, it is challenging to assess the distinct characteristics of time-series data, which results in difficulties in generating appropriate and reliable price forecasts. Numerous studies have been conducted on cryptocurrency price prediction using different deep learning-based algorithms, as these techniques have shown promise in capturing the complex patterns and trends in this market. This study proposes three types of recurrent neural networks: Long Short-Term Memory, Gated Recurrent Unit, and Bi-Directional LSTM, for exchange rate predictions of the three major cryptocurrencies in the world, as measured by their market capitalization: Bitcoin, Ethereum, and Litecoin. The experimental results on the three major cryptocurrencies using both Root Mean Squared Error and Mean Absolute Percentage Error demonstrate that the Bi-LSTM model performed better in prediction than LSTM and GRU, and can be considered the most effective algorithm for this task. Bi-LSTM presented the most accurate prediction compared to GRU and LSTM, with MAPE values of 0.036, 0.041, and 0.124 for BTC, LTC, and ETH, respectively. The study suggests that the proposed prediction models are accurate and reliable in forecasting cryptocurrency prices and can be beneficial for investors, traders, and researchers in the cryptocurrency market.