Example Concept: To enhance forecast accuracy in seasonal time series, consider using SARIMA (Seasonal Autoregressive Integrated Moving Average) models. SARIMA extends ARIMA by adding seasonal terms to account for repeating patterns at specific intervals. The model is defined by parameters (p, d, q) for non-seasonal parts and (P, D, Q, s) for seasonal components, where 's' is the seasonal period. By fitting a SARIMA model, you can capture both the trend and seasonal fluctuations, leading to more accurate forecasts.