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.