A suggestion, employ interpolation plus a randomization component. This is likely required to effectively address one (or more) of what I would frame as a missing data connection link.
Here is a source discussing various methods in use:
Sixty-eight papers (83%) described how they dealt with missing data in the analysis. Most of the papers excluded participants with missing data and performed a complete-case analysis (n = 54, 66%). Other papers used more sophisticated methods including multiple imputation (n = 5) or fully Bayesian modeling (n = 1). Methods known to produce biased results were also used, for example, Last Observation Carried Forward (n = 7), the missing indicator method (n = 1), and mean value substitution (n = 3). For the remaining 14 papers, the method used to handle missing data in the analysis was not stated.
Currently, you are apparently employing a bias 'Last Observation Carried Forward' methodology, where the carried forward value employed here is, more precisely, been interpolated adjusted.
A conservative approach, which elevates the variance, is exclusion (adjust the time series, excluding data gaps).