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)

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

Input ports

1. Type: Table
   Input data
   Table containing numeric target column to fit the SARIMA model.

Output ports

1. Type: Python
   SARIMA Model
   SARIMA model
2. Type: Table
   SARIMA Model Summary
   Table containing the coefficient statistics and the following evaluation metrics of the SARIMA model: RMSE MAE MAPE R2 Log Likelihood AIC BIC
3. Type: Table
   Residuals
   Table containing the residuals