- Type: TableInput dataTable containing numeric target column to fit the ARIMA model.
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) Note: This component requires a Python environment with StatsModels package installed. In this blog post we explain how to setup the KNIME Python extension: https://www.knime.com/blog/setting-up-the-knime-python-extension-revisited-for-python-30-and-20 Python script is used due to performance reasons. KNIME Autoregressive integrated moving average (ARIMA) extension provides an alternative ARIMA Learner node: https://kni.me/e/5_ZZ3nif8tLRjGji Required extensions: KNIME Python Integration (https://hub.knime.com/knime/extensions/org.knime.features.python2/latest) KNIME Quick Forms (https://hub.knime.com/knime/extensions/org.knime.features.js.quickforms/latest)
- Type: PythonARIMA ModelARIMA model
- Type: TableARIMA Model SummaryTable containing the coefficient statistics and the following evaluation metrics of the ARIMA model: RMSE MAE MAPE R2 Log Likelihood AIC BIC
- Type: TableResidualsTable containing the residuals
Used extensions & nodes
Created with KNIME Analytics Platform version 4.1.0