I have 32 months of historical data and I am testing forecasting methodologies. I assume that only 12 months of data are available, I forecast months 13-32, and then compare actuals for months 13-32 with my forecasts for months 13-32. The data at hand shows the number of monthly deaths, starting with a fixed population at time 0, and it follows a nice exponential decay curve over time. Plot y-axis shows number of deaths, y-axis shows month elapsed since time 0.
I’ve used traditional time series forecasting and have gotten good results with exponential state space models (ETS function from R package
feasts), with results that encompass the variability I’ve seen with this type of data. But I’m exploring other methodologies and am currently studying survival analysis, since I have a lot of variables that correlate with the probability of death and I have data for each study element showing progression stage each month.
So far in survival analysis I see that it is very useful for showing any effect of those variables on death rates (multivariate analysis, etc.), but at this stage I’m only interested in forecasting and simulating future curve paths in the hypothetical scenario of only having a partial curve to work with. Is survival analysis appropriate for forecasting from a partial curve? If so, how does one forecast future curve shape using survival probabilities and hazard rates (ignoring the variates)? Are there other methodologies besides ETS and survival that I should be exploring?
The below images show the survival probabilities plots, and the ETS model forecasts, with this dataset. Basically, is it possible to derive the sort of estimates using survival models that I’ve done with ETS?
Time-series forecasting using ETS: