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SARIMA Learner

Trains and generates a forecast using a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model. The SARIMA models captures temporal structures in time series data in the following components:

AR (AutoRegressive): Relationship between the current observation and a number (p) of lagged observations.
I (Integrated): Degree (d) of differencing required to make the time series stationary.
MA (Moving Average): Time series mean and the relationship between the current forecast error and a number (q) of lagged forecast errors.

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

Options

Settings

SARIMA settings to configure the parameters for the model.

Target Column: Table containing training data for fitting the SARIMA model, must contain a numeric target column with no missing values to be used for forecasting.
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 I Order (D): The number of times to apply seasonal differencing before training the model.
Seasonal MA Order (Q): The number of seasonal lagged forecast errors to be used in the model.
Seasonal Period: Specify the length of the Seasonal Period
Log-transform data for modeling: Optionally log your target column before model fitting and exponentiate the forecast before output. This may help reduce variance in the training data.
Forecast: Forecasts of the given time series h period ahead of the training data.
Generate in-samples dynamically: Check this box to use in-sample prediction as lagged values. Otherwise use true values.

Input Ports

: Table containing training data for fitting the SARIMA model, must contain a numeric target column with no missing values to be used for forecasting.

Output Ports

: Table containing forecasts for the configured column, the first value will be 1 timestamp ahead of the final training value used.
: In sample model prediction values and residuals i.e. difference between observed value and the predicted output.
: Table containing fitted model coefficients, variance of residuals (sigma2), and several model metrics along with their standard errors.
: Pickled model object that can be used by the SARIMA (Apply) node to generate different forecast lengths without refitting the model

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Installation

To use this node in KNIME, install the extension Time Series Extension from the below update site following our NodePit Product and Node Installation Guide:

v5.2

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 1.0.0.202409270142

On NodePit since: 2023-12-06

Last update: 2024-12-18

KNIME versions: From v5.2 to v5.2

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