LSTM doesnt find finer dependencies than the Random Forest model
Some (possibly stupid) ideas for combining the two models:


Make an average of the two forecasts
Instead of a naive average you use LSTM for estimating the coarse daily trend and the RF for the fine details, maybe like so:

a. Smooth (maybe with a loess or a kernel) the LSTM to get the daily trend

b. For each day, subtract the mean volume call from the prediction of the RF, to get the fine details

c. Add the forecast of two models 


General suggestions for improving your model:


Instead of training a model on the whole series of call volume, you might be better off feeding your model one day at a time, e.g. you cut the time series between 8 am and 5 pm every day. After all, you do not want your model to spend parameters on learning that no calls are made at night or during the weekend.
Add the day of the week as a parameter in the model, as well as some information on the call volume in the past (like previous day) if you think that there might be some time correlation between consecutive days.
If you think that the day of the week has a strong effect on the call volume (e.g. if each day has a special, recurring pattern) then you might consider creating a separate model for each day of the week.