SARIMA

SARIMA
Description	SARIMA is an abbreviation for Seasonal Autoregressive Integrated Moving Average. It is an extension of ARIMA used to model seasonal time series. SARIMA considers the events occurring at regular intervals and impacting the target variable similarly every time. Seasonality is the recurrence of impact-causing events at a fixed frequency.
Why to use	SARIMA is used to model seasonal time series
When to use	To model seasonal time-series data	When not to use	When the data does not contain seasonal factor
Prerequisites	Time-series data should not contain null or missing values.
Input	A time-series data with seasonality	Output	Forecasting Chart Predicted values with Standard Error
Statistical Methods Used	Average Root Mean Square Error	Limitations	--

ARIMA supports data with a trend but no seasonality. SARIMA explicitly handles the seasonal component in the univariate data. Thus, SARIMA effectively forecasts time series with univariate data containing trends and seasonality. While applying SARIMA, the hyperparameters of both the trend and seasonal elements are configured. These are,

Trend Elements: p, d, and q
Seasonal Elements: P, D, and Q