Forecasting multiple time series with a single model I have a dataset with sales numbers for ~500 different markets (assume different cities or regions) and need monthly forecasts for each market. Instead of building 500 different models, I'm interested in just training a single model with a single set of params to prevent me from having to manually feature engineer each market. 

Can someone point me to some resources for this problem? I went through the similar Stack questions and they weren't as helpful for this particularly.

Specifically, I know that LSTMs are sometimes used here, but would classical approaches be effective as well?
You can treat the markets as a categorical feature in a tree based (using decision tree as a weak leaner) ensemble model such as random forest or gradient boosting. Some applications are:

Sales for retail outlets of a major European Pharmacy
retailing company
Predicting the sales of
products from different outlets
Multi-Store multi-product forecasting