Seasonal ARIMA

Seasonal ARIMA (AutoRegressive Integrated Moving Average) is a statistical method used to forecast time series data that exhibits regular seasonal patterns, such as monthly sales or temperature fluctuations. It combines three components: autoregression (using past data points), differencing (to make data stationary), and moving averages (to smooth out short-term fluctuations). The "seasonal" aspect accounts for recurring patterns at specific intervals, like yearly or quarterly cycles. By identifying and modeling these patterns, seasonal ARIMA produces more accurate forecasts for data with predictable seasonal variations.