In field of economics (I think) we have ARIMA and GARCH for regularly spaced time series and Poisson, Hawkes for modeling point processes, so how about attempts for modeling irregularly (unevenly) spaced time series - are there (at least) any common practices?
(If you have some knowledge in this topic you can also expand the corresponding wiki article.)
I see irregular time series simply as series of pairs (value, time_of_event), so we have to model not only value to value dependencies but also value and time_of_event and timestamps themselves.
Edition (about missing values and irregular spaced time series) :
Answer to @Lucas Reis comment. If gaps between measurements or realizations variable are spaced due to (for example) Poisson process there is no much room for this kind of regularization, but it exists simple procedure :
t(i) is the i-th time index of variable x (i-th time of realization x), then define gaps between times of measurements as
g(i)=t(i)-t(i-1), then we discretize
g(i) using constant
dg(i)=floor(g(i)/c and create new time series with number of blank values between old observations from original time series
i+1 equal to dg(i), but the problem is that this procedure can easily produce time series with number of missing data much larger then number of observations, so the reasonable estimation of missing observations' values could be impossible and too large
c delete "time structure/time dependence etc." of analysed problem (extreme case is given by taking
c>=max(floor(g(i)/c)) which simply collapse irregularly spaced time series into regularly spaced
Edition2 (just for fun): Image accounting for missing values in irregularly spaced time series or even case of point process.