An Empirical Analysis of Forecasting Bitcoin and Gold Price Using ARIMA Model

Abstract

The autoregressive integrated moving average (ARIMA) model is a widely used technique for capturing past dependencies and trends in order to generate future predictions. This study presents a comparative analysis of the ARIMA models forecasting capabilities as applied to gold and Bitcoin prices. The methodology employed consists of obtaining historical price data, implementing machine learning techniques, fitting the ARIMA model, then validating its predictive ability using multiple error metrics. Our results indicated that the optimal ARIMA parameters for Bitcoin and gold are different, which emphasizes their different price behaviors. Additionally, the study examined implications for policy, including issues such as prices for CPUs and GPUs, the role of market dynamics, as well as the possibility for price manipulation, which is of special relevance for cryptocurrencies that exist outside the mainstream. The study also suggests potential directions for future research, such as applying advanced machine-learning techniques and adopting cross-validation. This research offers important insights regarding Bitcoin and gold price dynamics and demonstrates the applicability of the ARIMA model for financial forecasting while demonstrating the necessity of further investigation into more refined predictive models.