I have a dataset with 2 discrete predictors and I need to forecast demand of workforce required. The features basically describe applicant behaviour(stage of application e.g. online assessment, pending invite, interview round 1, under review etc). There's another predictor variable that states time frame but in categories e.g. been in a certain stage of application for >1 and <10 days, > 10 days. As seen above, the discrete predictors are not binary(e.g. Male/Female) and could be divided within multiple categories. I have data back from 2019-Now.
I need to forecast for anywhere upto 6 months. I'm not sure if this holds any importance but the data will be analysed on a weekly basis.
Since this is a demand forecasting problem, I thought an ARIMA/Holt-Winters approach would work but I'm unsure if the size of the dataset is large enough(roughly 2 and a half years). Since I have discreet features as well, I thought a random forest model might work better?
The data is quite seasonal as well, and that could serve as a key deciding factor as well.
I would appreciate any suggestions one could give me in terms of which model I should look into? If anyone has more suggestions than random forest or arima and holt winters then that would be of great usage as well!