ts2vec additional use case for non-uniformly sampled ts input #39
Description
hi!
Just read your paper and am super interested in it. The results are really promising on uniformly sampled input data, but it seems like from the way that the method itself was conceptualized and structured it should/could be robust to non-uniformly sampled ts input. By non-uniformly sampled time-series, I mean a time series where not all time points are sampled in the input. Using a cancer patient getting X-rays for example, they might get it at irregular intervals. For example, patient 1 might get the radiology at day 0, day 7, day 25, day 47; patient 2 might do so at day 0, day 23, day 56, day 59, day 70; etc..
One obvious way to adapt this to the ts2vec format is to bin the days, say 20 days per interval; fill in the missing data with some imputations; then converts that into a uniformly sampled ts. However, do you guys see any obvious ways one can use ts2vec in a way that does not require that approximation? Thanks!