Bitcoin Halving Prediction: A Quantitative Approach
Using an array of quantitative techniques, this paper aims to predict the future date of Bitcoin’s halving, a pivotal event affecting its inflation rate. Employing double exponential smoothing, autoregressive integrated moving average, and vector autoregression, we analyze historical halving dates to construct predictive models. Our research leverages a robust dataset of Bitcoin halvings from 2012 to 2024, providing substantial data points for statistical inferences. Through rigorous analysis and validation, we identify patterns and relationships within the data, enabling us to anticipate the upcoming halving. Our findings contribute to the ongoing debate on Bitcoin’s market dynamics, aiding traders, investors, and stakeholders in navigating its volatile fluctuations.
