An ARIMA model is defined by three parameters: p (autoregressive part), d (differencing part), and q (moving average part). The process involves:
Identifying the appropriate values of p, d, and q through model selection criteria such as AIC (Akaike Information Criterion) or BIC (Bayesian Information Criterion). Estimating the model parameters using historical data. Validating the model through residual analysis and diagnostic checks.