Hello all, during the development of a project, we faced some problems concerning the Partial Autocorrelation Function (PACF), when using the Splunk Machine Learning Toolkit version 3.1.0.
Theoretically, the PACF gives us values in the range -1 to 1 and allows us to analyze the dependence and behavior between different lags.
If we run the default function, we obtained some lags for which the observed absolute PACF value was greater than 1.
We thought it could be the case that for huge lags, some error occurred. But we also obtained a similar trouble for lags < 50.
Then we decided to specify the method that we use to calculate it – Yule-Walker with or without bias correction, Ordinary Least Squares, Levison-Durbin with or without bias correction. For some of them we got once again lags for which the absolute PACF value was greater than 1, but fortunately, we also obtained decent values – especially using OLS method.
Does anyone has experienced similar results or knows what is the reason for them to happen?
We suspect that it might be caused by error accumulation since all methods mentioned are numerical methods that suppose some type of convergence.
(Also – funny fact – if one checks the manual, it will see in the description of the PACF function a plot where PACF takes a maximum absolute value of almost 6 (?), which is theoretically impossible. Here is the link:
https://docs.splunk.com/Documentation/MLApp/3.1.0/User/ForecastTimeSeries#PACF_Residual:_partial_autocorrelation_function_residual_chart )
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