Forecasting Bitcoin Trends Based on the ARIMA Model

Abstract

This research paper aims to conduct a time series forecasting of the bitcoin mean weighted price using the data from Kaggle. The data has a one-minute resolution and includes the following variables: timestamp, open, high, low, close, volume (BTC), volume (currency), and weighted price. Data analysis was achieved using R, a statistical computing and graphics programming language. The main findings of this research paper were that the bitcoin mean weighted price had a strong upward trend and exhibited high volatility over time. The time series also had weak seasonal and significant random components, indicating periodic fluctuations and noise in the data. Four years of data were used to estimate the mean change for the following month. The results indicate that while the expected value may rise somewhat, it will do so with significant variability and unpredictability. The main implications of this research paper were that there was a potential for profit or loss depending on the timing and strategy of buying or selling bitcoins.