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

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.
Estimation Method: The log likelihood to maximize. The default method is css-mle.%%00010%%00010css-mle: maximize the conditional sum of squares likelihood and use these values as starting values to compute the exact likelihood via the Kalman filter.%%00010mle: maximize the exact likelihood via the Kalman filter.%%00010css: maximize the conditional sum of squares likelihood.

Input Ports

: Table containing numeric target column to fit the ARIMA model.

Output Ports

: ARIMA model
: Table containing the coefficient statistics and the following evaluation metrics of the ARIMA model: RMSE MAE MAPE R2 Log Likelihood AIC BIC
: Table containing the residuals

ARIMA Learner

Options

Input Ports

Output Ports

Nodes

Extensions

Links

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