Handling missing value


Currently, NumXL allows observations with missing values at either ends of the time series, but not intermediate observations.

In practice, having a sparse runs of observations with missing values is common, so would it be possible if we can use the whole time series, and NumXL will only use the non-missing observations?

By doing so, we avoid any noise produced by the missing values imputation methods.





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