For practical time series forecasting with missing data, you need to predict both whether observations will happen and what their values are—treating these as a coupled problem rather than assuming future timestamps are known.
This paper tackles incomplete time series forecasting by jointly predicting whether future observations will occur and what their values will be. Most existing methods assume future timestamps are known, which isn't realistic.