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)

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)

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

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Table containing numeric target column to fit the ARIMA model.

Output Ports

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

Nodes

Extensions

Links

Download

To use this component in KNIME, download it from the below URL and open it in KNIME:

Download Component
On NodePit since: 2022-06-07
Last update: 2024-11-18
Created with KNIME version: v4.1.0

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