ARIMA Learner

Trains an AutoRegressive Integrated Moving Average (ARIMA) model. ARIMA model captures 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

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

Input ports

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

Output ports

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