Digital Currency Price Prediction using Machine Learning

Abstract

Coming into the 21st century, the views towards finance and investments have drastically shifted from traditional assets like gold, land, and property to assets in the digital domain, like investing in various stocks, mutual funds, currencies, etc. The more recent developments of technology and newer investment options have brought various block-chained digital currencies to the forefront. Digital currency options offer massive gains but are very dynamic and quick-moving investments, making predicting the future values for maximum gains difficult, thereby reducing the confidence of even seasoned investors. While digital currencies saw massive gains in the past decade, research in deep learning has seen equivalent growth in terms of efficiency, required computation, and prediction rate. In this project, we explored various digital currencies, using multiple machine learning models from extended trees to time series analysis, only to use Long Short-Term Memory (LSTM) model to get accurate predictions for the tested parameters. While there is a risk of overfitting the dataset, given that this project uses APIs to get real-time data, the risk is mitigated by the fact that it’s giving precise outputs for unseen data.