A Novel Prediction Model for Cryptocurrency Trend Analysis Based on Time Series Data by Using Machine Learning Techniques

Abstract

Bitcoin is a form of cryptocurrency that has come to be a famous inventory marketplace funding and it's been gradually growing in current years, and every now and then falling without warning, at the inventory marketplace. Because of its fluctuations, an automatic device for predicting bitcoin at the inventory marketplace is required. However, due to its volatility, traders will want a prediction device to assist them make funding selections in bitcoin or different cryptocurrencies. In this paper, Deep gaining knowledge of mechanisms like Recurrent Neural Network (RNN) and Long short-time period memory (LSTM) is proposed to broaden a version to forecast the bitcoin charge fashion withinside the marketplace. Finally, the predictions end result for the Bitcoin charge fashion are supplied over the subsequent 15, 30, and 60 days. Each version is evaluated in phrases of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) forecasting blunders values. The LSTM version is observed to be the higher mechanism for time-collection cryptocurrency charge prediction, however it takes longer to compile. The goal of this project is to show how a trained machine model can predict the price of a cryptocurrency if we give the right amount of data and computational power. It displays a graph with the predicted values. The most popular technology is the kind of technological solution that could help mankind predict future events. With vast amount of data being generated and recorded on a daily basis, we have finally come close to an era where predictions can be accurate and be generated based on concrete factual data. Furthermore, with the rise of the crypto digital era more heads have turned towards the digital market for investments. This gives us the opportunity to create a model capable of predicting crypto currencies primarily Bitcoin. This can be accomplished by using a series of machine learning techniques and methodologies.