Choice of time-series model for store sales prediction I have a data set of weekly sales for a range of stores (all belonging to one company). I am trying to predict weekly/monthly use of several ingredients in the individual stores. The choice for what type of model to use seems to be between Holt Winter (or state space models more generally) and the class of ARIMA models.
I have made a range of analyses testing which type works best over the entire dataset. That is, I have for example looked at what model describe any given series best and then counted which describes the most series best. This has been done using MSE, MAPE and other measures.
However, I am not sure whether I should simply determine which model is best for any given individual series and then use that. The reasons I haven't done that is because it seems more intuitive to use the same model for all series.
And so my question is, is there any particular theoretical or practical reason, why I would want to choose one method or the other?
 A: I will elaborate one point not mentioned by the other answers.
With many series for different stores/products, there might be competition/substitution effects so you could want to use some form of hierarchical forecasting. Specifically, some product might be possible substitues for other product, leading to negative correlations in sales. There might be seasonality effects common for all/most products, leading to positive correlations. I would start investigation of such effects maybe with a principal components analysis. 
If such effects are important (they probably are), some kind of hierarchical prediction could be much better than univariate modeling. Multiple approaches are possible. One way, I used in one project, was first modeling total sales, and then modeling proportions of total sales. That would be top/down, one could also get the other way, start with individual series, and then correcting them if the total gets unrealistic. This is discussed in some other post on this site, like Hierarchical time-series forecasting with complex aggregation constraints  or  Single prediction vs. summing more granular n-step ahead predictions 
There is now even an R package for hierarchical forecasting on CRAN, hts https://CRAN.R-project.org/package=hts  Its documentation contains references you should have a look at. 
A: ARIMA models easily incorporate empirically identified pulses ,level shifts and local time trends while incorporating parameter and error variance changes. HW models are a fixed procedure lacking the robustness and adaptability of ARIMA models with Intervention Detection and are bloated by incorporating unnecessary/non-significant parameters. Additionally ARIMA models easily incorporate user-specified causals morphing into Transfer Function Models. Why settle for an assumed model form like HW when diagnostics can lead to better modelling. Better modelling often includes day-of-week effects, holiday effects, monthly effects, weekly effects , day-of-the-month effects et al . You might want to look at this reference http://www.autobox.com/cms/index.php/blog/entry/advantages-and-disadvantages-of-using-monthly-weekly-and-daily-data to more fully understand why you need to be using daily data
A: For this type of problem of model selection / hyperparameter-optimization, I would recommend you look into cross-validation approaches. Especially since your primary goal seems to be out-of-sample prediction, you want to be careful about overfitting your training data.
I can't think of a reason why you couldn't have different models for different items, but you may also want to share information across the different models (perhaps some sort of hierarchical setup), which may be more difficult or impossible if the models are incompatible.
A: It is useful to know which time-horizon you care about (a month, a week or a day in advance?), and how much data you have (can you reliably estimate yearly seasonality?). 
Personally, I've found ARIMA to be unintuitive and full of traps, and I haven't had much success with it. If you have daily data and care about daily fluctuations, then it would probably the right choice anyway. 
But whatever you end up doing, my suggestion is to start with a "simple" regression model, include some yearly seasonality (some cyclic splines), holidays and a trend, ideally set it up with a hierarchical structure like mentioned in another answer. The coefficients will be understandable, it's quite easy to start with simple and expand. 
Crossvalidation in timeseries doesn't work, so just create a couple of windows of a reasonable time period (at least two years if you want to have yearly seasonality and are not using a hierarchical model) and evaluate your method by it's forecast of the next week, or month, or year, whatever range you care about. The evaluation is not straightforward (do you care about a forecast in the near future more than a later one?) and should also reflect your business situation. 
