I have an univariate time series with daily values of temperature over 60 years with missing values. The size of the gaps varies from a day to a couple of years. My goal is:
- to impute the missing values
- to estimate the uncertainty at each day to generate an heatmap (x-axis: days and y-axis: year).
My first thought is to fit a curve going through all the points and then calculate the absolute difference between the true value and the predicted value at each day. I am conscious that I won't have any uncertainty estimation when a value is missing.
Is my approach correct? If yes, how can I do this? If no, do you have any better ideas?
- There is definitely a trend and seasonal effect in the time series
- I am ready to assume stationarity
- I am coding in R