Description

This Master Prompt focuses on classical statistical and econometric forecasting methodologies for analysing temporal patterns, modelling stochastic relationships, and estimating future observations using mathematically established techniques. It covers naïve and seasonal naïve forecasting, moving averages, exponential smoothing, Holt and Holt–Winters methods, ETS models, autoregressive and moving-average processes, ARIMA, SARIMA, ARIMAX, vector autoregression, state-space models, Kalman filtering, dynamic regression, and selected econometric forecasting frameworks. The theme addresses trend and seasonal decomposition, differencing, stationarity testing, parameter estimation, model identification, residual diagnostics, structural breaks, exogenous variables, forecast intervals, and multi-step forecasting strategies. It emphasises transparent model specification, statistical assumptions, interpretability, comparative benchmarking, forecast accuracy, and the continuing importance of classical statistical methods as reliable baselines for advanced artificial intelligence forecasting systems.