Generate data from a known model, specify a state-space model containing unknown parameters corresponding to the data generating process, and then fit the state-space model to the data.
The property values in an existing model are retrievable. Working with models resembles working with struct arrays because you can access model properties using dot notation. That is, type
Create a time-invariant state-space model by passing a parameter-mapping function describing the model to ssm (that is, implicitly create a state-space model). The state model is AR(1)
Fit a regression model with multiplicative ARIMA errors to data using estimate .
Estimate a multiplicative seasonal ARIMA model using estimate . The time series is monthly international airline passenger numbers from 1949 to 1960.
You can also modify model properties using dot notation. For example, consider this AR(2) specification:
Estimate a composite conditional mean and variance model using estimate .
Create a time-invariant, state-space model containing unknown parameter values using ssm .
Estimate the parameters of a VAR(4) model. The response series are quarterly measures of the consumer price index (CPI) and the unemployment rate.