Seasonal ARMA

Seasonal ARMA (AutoRegressive Moving Average) models are statistical tools used to analyze and forecast data that exhibit patterns repeating at regular intervals, such as monthly sales or daily temperatures. They capture both short-term fluctuations and seasonal patterns by combining two components: autoregression (dependence on past values) and moving averages (dependence on past errors). The "seasonal" aspect specifically models repeating cycles, allowing for more accurate predictions in data with regular seasonal behavior. This approach helps identify underlying trends and seasonal effects, improving forecasting accuracy for complex, periodically repeating data.