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)
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 Python Integration
KNIME Quick Forms
Get this component from the following link: Download
Auto ARIMA Learner consists of the following 50 nodes(s):
Auto ARIMA Learner contains nodes provided by the following 6 plugin(s):
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to firstname.lastname@example.org, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.