Trains AutoRegressive Integrated Moving Average (ARIMA) models and returns the best model according to the search criterion (AIC, BIC) within the provided constraints (max p,d,q). 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. *Note that the (p,d,q) values of the selected model can be found in the model summary output table. 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
- Type: TablePort 1Table containing numeric target column to fit the ARIMA model.