Forecasting Future Occurrences With Random Forest

I'm currently exploring the use of Random Forests to predict future values of occurrences (my ARIMA model gave me really bad forecasting so I'm trying to evaluate other options). I

Solution 1:

Adressing your first question, random forest might tend to overfit, but that should be checked when comparing the MAE, MSE, RMSE of your test set. What do you mean with accuracy? Your R square? However, the way to work with models is to usually make them overfit at first, so you have a decent accuracy/mse/rmse and later perform regularization techniques to deal with this overfitting by setting a high min_child_weight or low max_depth, a high n_estimators is also good.

Secondly, to use your model to predict future values, you need to use the exact same model you trained, with the dataset you want to make your prediction on. Of course the features that were given in train must match the inputs that will be given when doing the forecasting. Furthermore, keep in mind that as time passes, this new information will be very valuable to improve your model by adding this new information to your train dataset.

forecasting = rf.predict(dataset_to_be_forecasted)