Dealing with NaN in idnlgrey

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Andres on 16 Jun 2012
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 ?

Answers (1)

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


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