SARIMA Learner

Trains a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model. SARIMA models capture temporal structures in time series data in the following components:
- AR: Relationship between the current observation and a number (p) of lagged observations
- I: Degree (d) of differencing required to make the time series stationary
- MA: Time series mean and the relationship between the current forecast error and a number (q) of lagged forecast errors

*Seasonal versions of these operate similarly with lag intervals equal to the seasonal period (S).

Additionally, coefficent statistics and residuals are provided as table outputs.

Model Summary metrics:
RMSE (Root Mean Square Error)
MAE (Mean Absolute Error)
MAPE (Mean Absolute Percentage Error)
*will be missing if zeroes in target
R2 (Coefficient of Determination)
Log Likelihood
AIC (Akaike Information Criterion)
BIC (Bayesian Information Criterion)

If you encoutner errors please verify that
Preferances > KNIME > Python (labs) > Python environment configuration
is set to bundled

Options

Target Column

The numeric column to fit the model.

AR Order (p)

The number of lagged observations to be used in the model.

I Order (d)

The number of times to apply differencing before training the model.

MA Order (q)

The number of lagged forecast errors to be used in the model.

Seasonal AR Order (P)

The number of seasonally lagged observations to be used in the model.

Seasonal MA Order (Q)

The number of seasonal lagged forecast errors to be used in the model.

Seasonal I Order (D)

The number of times to apply seasonal differencing before training the model.

Seasonal Period

Specify the Length of the Seasonal Period

Nodes

• Math Formula8 ×
• Integer Configuration7 ×
• GroupBy5 ×
• Joiner5 ×
• Breakpoint3 ×
• Show all 25 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Javasnippet
• FeatureKNIME Math Expression (JEP)
• FeatureKNIME Quick Forms
• FeatureKNIME Statistics Nodes

Links

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