Bitcoin Price Forecasting Using LSTM

Abstract

The volatility and complexity of Bitcoin make it a challenging task to accurately predict its price. While past research has implemented machine learning to enhance the precision of Bitcoin price prediction, limited attention has been given to examining the viability of employing diverse modeling techniques to datasets with varying data structures and dimensional attributes. In order to forecast Bitcoin prices using machine learning techniques at different intervals, this study initiates by categorizing Bitcoin prices into daily prices and highfrequency prices. This project aims to predict the price of Bitcoin using machine learning techniques, specifically the Random Forest Classifier algorithm.