Analyze ARIMA Residuals

This component analyzes the residuals of an ARIMA (AutoRegressive Integrated Moving Average) model by
1. visualizing auto correlation of the residuals
2. performing Ljung-Box test of autocorrelation at lags 1-10
3. visualizing residuals in a line plot
4. calculating the four first central moments of the residuals
5. performing Jarque-Bera test of normality

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Preferances > KNIME > Python (labs) > Python environment configuration
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Options

Select column:

Residuals of an ARIMA model to analyze

Number of ARIMA parameters (degrees of freedom):

Degrees of freedom consumed by the ARIMA model depending on the number of parameters (p+d+q) in it. For example, if you have an ARIMA (1,0,1) model, the degrees of freedom 2. This value is needed to perform the Ljung-Box test of stationarity for the residuals.

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