Association rule learning has a fair bit of material based around the correlation of products purchased on the same order/at the same time. However I'd like to discover if there is a method for identifying such a relationship between products that are ordered near each other, but not together.
Say for example a customer purchases a pencil in week one, but later purchases an eraser in week two. Then a year later they do the same thing. But then also apply this across all customers who show similar behaviour. I would then aim to combine this with the correlation coefficient of products purchased in an order together.
I considered giving each of the dates before or after a purchase a diminishing weight (1/n days) and then comparing the aggregate date value of the customer purchase date history with a product A against product B. But this seems like a fairly drawn out method.
Just looking for a process to achieve this, but python hints would also help.
Thanks in advance.