This example shows how to estimate Autoregressive Integrated Moving Average or ARIMA models. Models of time series containing non-stationary trends (seasonality) are sometimes required. One category of such models are the ARIMA models. These models contain a fixed integrator in the noise source. Thus, if the governing equation of an ARMA model is expressed as where the term represents the discrete-time integrator. Similarly, you can formulate the equations for ARI and ARIX models. Using time-series model estimation commands The estimation approach does not account any constant offsets in the time-series data. The ability to introduce noise integrator is not limited to time-series data alone. You can do so also for input-output models where the disturbances might be subject to seasonality. One example is the polynomial models of ARIMAX structure: See the Estimate an ARI model for a scalar time-series with linear trend. Estimate a multivariate time-series model such that the noise integration is present in only one of the two time series. If the outputs were coupled ( |