Dealing with NaN in idnlgrey

Asked by Andres on 16 Jun 2012

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Anyone could tell me how to use the pem(Prediction error estimate) function to estimate a non linear grey model if I have NaN (missing values) in some points of the observations ?

Thanks!

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Answer by Rajiv Singh on 27 Jun 2012

Missing data cannot be directly handled by an estimation routine. You must "fix" your data appropriately in advance. Some things to try:

1. See MISDATA where you can fit in the missing values if you have some knowledge of the underlying process that generated the data. See: http://www.mathworks.in/help/toolbox/ident/ref/misdata.html

2. If the missing samples are found in small isolated clusters, you can split the data into independent NaN-free segments. Represent each segment as an iddata object. Then MERGE the resulting data objects into one "multi-experiment" data object. See: http://www.mathworks.in/help/toolbox/ident/ref/mergeiddata.html

The merged multi-experiment data object can then be used for estimation.

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