Predictive Modelling: Forecasting the Future with Computational Statistics

This article examines predictive modelling through the lens of computational statistics, focusing on how historical data can be used to estimate future or unobserved outcomes. It covers regression, tree-based ensemble methods, exponential smoothing, autoregressive models and Bayesian forecasting. Particular attention is given to model estimation, regularisation, resampling, temporal dependence and probability distributions. The article also addresses practical forecasting challenges, including data leakage, rolling evaluation, model calibration, asymmetric loss functions and changes in data-generating processes. Numerical examples demonstrate forecast evaluation, prediction intervals and decision thresholds. The study emphasises reproducibility, uncertainty quantification, appropriate benchmarking and continuous model monitoring. Overall, it provides a practical statistical framework for developing, evaluating and applying credible predictive models under changing conditions.