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)
Note: This component requires a Python environment with StatsModels package installed. In this blog post we explain how to setup the KNIME Python extension:
knime.com/blog/setting-up-the-knime-python-extension-revisited-for-python-30-and-20
To use this component in KNIME, download it from the below URL and open it in KNIME:
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