Predicting the Price of Bitcoin: Using Machine Learning Time Series Methods

Abstract

Cryptocurrencies have greatly increased their Bitcoin-led popularity in recent years due to increased trading volumes and massive capitalization in the market. These cryptographic forms of money are not just utilized for exchanging nowadays, they are additionally acknowledged for fiscal exchanges. It appears to be evident that financial specialists, dealers and people, in general, are progressively intrigued by bitcoin and altcoins as costs rise and the arrival on ventures made increments. This examination centres around applying estimate models that will make precise value forecasts for cryptographic forms of money. The data were taken from two different exchanges and evaluated as combined dataset. As a result of the evaluation, it was determined that the prices were close to each other in terms of value and the data were combined. We obtained the daily time series data by determining the Bitcoin weighted price as a dependent variable and Open, Close, High, Low and Volume as independent variable. We predicted the next 6 months with ARIMA, LSTM and XGBoost methods. We compared these estimates using MSE, MAE, MAPE and R squared performance metrics. LSTM is the model with the best R squared value of 29.7%. In the process performed by taking the average of LSTM, XGBoost and ARIMA performed with the name of Average ML method, the R square value was found to be 41.6% as a much better result than LSTM.