Trains and generates a forecast using a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model. The SARIMA models captures temporal structures in time series data in the following components:
- AR (AutoRegressive): Relationship between the current observation and a number (p) of lagged observations.
- I (Integrated): Degree (d) of differencing required to make the time series stationary.
- MA (Moving Average): Time series mean and the relationship between the current forecast error and a number (q) of lagged forecast errors.
Seasonal versions of these components operate similarly, with lag intervals equal to the seasonal period (S).
Options
Settings
SARIMA settings to configure the parameters for the model.
Input Ports
Table containing training data for fitting the SARIMA model, must contain a numeric target column with no missing values to be used for forecasting.
Output Ports
Table containing forecasts for the configured column, the first value will be 1 timestamp ahead of the final training value used.
In sample model prediction values and residuals i.e. difference between observed value and the predicted output.
Table containing fitted model coefficients, variance of residuals (sigma2), and several model metrics along with their standard errors.
Pickled model object that can be used by the SARIMA (Apply) node to generate different forecast lengths without refitting the model
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