LSTM vs ARIMA for demand prediction I'm new to the field of time series prediction. 
I'm looking for a demand prediction model to predict when the product will be sold out from the online supermarket (when the supply is known in advance).

I know that LSTM and ARIMA are the "best" model for time series prediction. Is there any other model for demand prediction which could be better? 

and what are the con and pros for those model for demand prediction?

Also, can someone give me some time frames to developing this kind of model? due I need to consider anything else than things in the regular model (like hyper-parameters tuning and etc.)
ARIMA models are basically linear models, so they can only work if the relation is linear (or linear integrated). They are easy to estimate.

LSTM can model more or less any relationship, but at the cost of additional computation and require more data to be trained.

So start with ARIMA if you don't know anything about our data, if it doesn't work, use a simple LSTM model and then complexity it to match what you need with the test set.