Model the time series using vector autoregression (VAR) model.
- Time series model: The VAR model fitted to input time series.
- Forecast: The forecast time series.
- Fitted values: The values that the model was actually fitted to, equals to original values - residuals.
- Residuals: The errors the model made at each step.
Using this widget, you can model the time series using VAR model.
- Model’s name. By default, the name is derived from the model and its parameters.
- Desired model order (number of parameters).
- If other than None, optimize the number of model parameters (up to the value selected in (2)) with the selected information criterion (one of: AIC, BIC, HQIC, FPE, or a mix thereof).
- Choose this option to add additional “trend” columns to the data:
- Constant: a single column of ones is added
- Constant and linear: a column of ones and a column of linearly increasing numbers are added
- Constant, linear and quadratic: an additional column of quadratics is added
- Number of forecast steps the model should output, along with the desired confidence intervals values at each step.
ARIMA Model, Model Evaluation