Which ML techniques to use for demand forecasting in a retail company?

machine_learning
retail
data_science
forecasting
demand

#1

Suppose a retail company has 1000 products and 1000 stores, if I want to forecast demand of each product at store and day level (I mean forecasting for each and every day of the year), which ML technique is the preferred one?

I thought of time series forecasting first but then doing time series forecasting for each product at each store looks extremely time consuming. I am not really sure about applying Linear Regression either as it might violate the auto-correlation assumption. So, which ML techniques do you think are best applicable in this scenario?