For a while now I used to forecast integer/count time series as I would do for any other continuous time series, meaning : I use models like ARIMA, ETS, THETA, TBATS ... And then I simply round the results. So I wonder is there some models designed specifically for count time series ? Are they more efficient than the previous models ?
Count time series models are handled in the tscount and acp packages. ZIM provides for Zero-Inflated Models for count time series. tsintermittent implements various models for analysing and forecasting intermittent demand time series.
Then see what models are implemented, and check the references. The
tscount package has a nice vignette on analysing count time using using GLMs.
As to whether they are more efficient, that depends on the data and what you mean by efficiency. If a count time series model is a good fit, then it will be more efficient (in the statistical sense) to use it. It may not be more computationally efficient depending on how it is coded.
The comments suggested you mean accurate rather than efficient. The only way to answer that is to try it and see.