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Seasonal Autoregressive integrated Moving Average with Exogenous Regressors (SARIMAX)
The Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX) is an extension of the SARIMA model that also includes the modeling of exogenous variables.
Exogenous variables are also called covariates and can be thought of as parallel input sequences that have observations at the same time steps as the original series. The primary series may be referred to as endogenous data to contrast it from the exogenous sequence(s). The observations for exogenous variables are included in the model directly at each time step and are not modeled in the same way as the primary endogenous sequence (e.g. as an AR, MA, etc. process).
The SARIMAX method can also be used to model the subsumed models with exogenous variables, such as ARX, MAX, ARMAX, and ARIMAX.
The method is suitable for univariate time series with trend and/or seasonal components and exogenous variables.
Below is an example of SARIMAX code implementation

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Data Science
Related
Autoregression (AR)
Autoregressive Integrated Moving Average (ARIMA)
Moving Average (MR)
Autoregressive Moving Average (ARMA)
Seasonal Autoregressive Integrated Moving Average (SARIMA)
Seasonal Autoregressive integrated Moving Average with Exogenous Regressors (SARIMAX)
Simple Exponential Smoothing (SES)
Holt Winter's Exponential Smoothing (HWES)