Handling time series data with gaps
I'd represent actual repeated values differently from missing data. The latter is straightforward; the former I'd interpolate with Gaussian process regression. This way you'll be able to get error bars like so:



Note how the sample functions (and thus error bars) expand and contract as you depart and approach the measurements, as you would intuitively expect.