Time series grouping for detecting market cannibalism I'm dealing with daily sales of products in a supermarket. 
Together with the historical data I have promotions for each product and weather data. What I lack is data about stock in and stock out of products.
In particular the stock out I miss for a product $i$ at time $t$  (say Pepsi)
 $Y_{i_t}$ could correspond to a spike in sales for a similar product $j$ at the same time $t$ (say Coke) $Y_{j_t}$ , where $t$ denotes a day. 
Basically I want to find clusters of the same kind of products so that I know how to redistribute the items over similar products, avoiding other stock outs of the similar products, what is called Market Cannibalism
I don't have data on the amount of time the supermarket need to restock the items stocked out, but I hope they don't restock the items daily, since at that point I could not use the information for forecasting.
Thank you :)
 A: I'm mostly answering how and why the available data can be sufficient. This is what I understand your question to be about.
Don't worry too much, you have the most important data available. I'm not sure if you have a background in statistics and time series and have now been assigned this task or if you have a background in supply chain management. Please don't feel insulted if I explain familiar concepts to you.
You do not have data about how much stock came in at what moment. There are a couple of ways to manage stocks, one or a combination of which will probably have been used in the past:


*

*It could have been an economic order quantity (EOC) per SKU (which determines the restocking frequency at a given level of demand) combined with a safety stock per SKU.

*It could have been a kanban system with two containers where you order a new container as soon as you open the last one in stock. (If deliveries are in fixed intervals, not every SKU can be on its EOC. Some will typically be on a kanban system.)

*Some inventory could be managed by your suppliers. (This happens when sales for a particular product are very difficult to predict and the supplier has better demand forecasts)


Even if you do not have the past data about stock intake, you should be able to find out with which policies this supermarket operates. This tells you enough about stock intake to construct your model and get actionable recommendations from it.
Regarding stockouts, you do not crucially need data about past stockouts per SKU per day I think. You can find your clusters using data about sales per day per SKU. This data you should have, right? (I couldn't imagine a supermarket who doesn't log this data.) I don't understand why you would want to find clusters based on stockouts in the first place. Stockouts are a delayed consequence of exceptional demand, at best a proxy for measuring demand. You have the direct data of this demand (daily sales), base your clusters on that data.
