SARIMA Learner

Trains a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model. SARIMA models capture 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

*Seasonal versions of these operate similarly with lag intervals equal to the seasonal period (S).

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:

knime.com/blog/setting-up-the-knime-python-extension-revisited-for-python-30-and-20

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.
Seasonal AR Order (P)
The number of seasonally lagged observations to be used in the model.
Seasonal MA Order (Q)
The number of seasonal lagged forecast errors to be used in the model.
Seasonal I Order (D)
The number of times to apply seasonal differencing before training the model.
Seasonal Period
Specify the Length of the Seasonal Period

Input Ports

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Table containing numeric target column to fit the SARIMA model.

Output Ports

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SARIMA model
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Table containing the coefficient statistics and the following evaluation metrics of the SARIMA 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: 2024-11-21
Created with KNIME version: v4.4.0

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