Model selection based on demand pattern of series

[SSMK-wq]

I am new to time series tasked to work on a time series forecasting project in my office.

We have to forecast the demand for 10 products at each store level (no hierarchy). Just at store level is enough... We have 20 stores.
So, we have a total of 200 series. We have exogenous variables such as day, week day, month, weather etc as input variables to the series

Now, the problem is each product may exhibit a different demand pattern in each store. For example "product A" in store ID=1 may be fast moving and the same product in store ID=3 could be slow moving.

Is there anyway that Nixtla already selects/proposes model based on its understanding of demand pattern of the series?

Or the only way is , I should preconfigure the models based on my understanding of demand? For ex - Random Forest, Croston (for intermittent demand), seasonal naive (sporadic or no demand) etc.

So, the only way is even though I know croston is not an appropriate model for constant demand, high runners, code will still run through that model, costing us time and resources?

Is there any elegant existing solution using Nixtla which will pick the model based on its understanding of the demand pattern of product in each store?

Or the only solution is to pre-configure the list of models? For ex - let's say I provide 10 model names to the model dictionary, then all the 200 series will be tested for all the 10 models? Is this the only way?

1. How do time series experts here usually do this in a efficient way?

2. Does Nixtla experts have any advice on how this problem can be approached?