Is it possible to interpolate missing values on a MSTL fitting? I have hourly sensor data over several years that has missing observations (missing at random due to sensor failure. Observations are missing in contiguous chunks up to 1 month duration). The data has a very strong daily and weekly seasonality. Fitting with the missing timepoints removed gives an ok fit for the existing data, but I'm not sure how to get the fitted model to interpolate the missing values. Any advice? Edit: Im trying to avoid the flat interpolation of croston etc, and have something that incorporates the seasonality

Hi @James Farnell my suggestion would be to interpolate the values (can be anything linear, splines, etc.) and then create a dummy variable to indicate that you have imputed for that particular timeslot