I am working on teaching myself some forecasting techniques that I can use in the future.

Imagine a shopping mall. The mall contains many shops which each sell different products. 

I have a bunch of data relating to each shop:

> 
> Number of products they sell
> 
> 
> Price of these products
> 
> 
> Historical sales figures
> 
> 
> The type of shop
> 
> 
> How many customers visit each shop in a given window
> 
> 
> How many days a week the shop is open for
> 
> 
> ...
> 
> 
> etc
> 
> 
> 

I am looking for a way to predict based on the above how much revenue a given shop is likely to take in a month?

E.g. A computer shop with 500 products that is open everyday and took 5000, 3700, 4900,...,6000 is estimated to bring in 5400USD next month.

I have had some limited success in the past using time series methods but I feel like there is not really any seasonal growth for a large number of shops and I am wondering about other methods.

Also I know that all of the data I have may not be relevant (for instance the number of products they sell may not make any difference to the predicted values of sales going forward) but I have a lot of data so want to incorporate some of it into the model rather than just a straight time series.

Can anyone suggest any types of models that might be appropriate?

I have heard about logistic regression and linear regression and have vaguely looked at the former and seem a bit of the latter are these useful?

If not could someone tell me what kind of models I could be researching into in order to build a model that utilises the data as I don't think I am making the most of the large amounts of data I have.

Thanks!