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I have a question regarding Holt Winters method.

I have an order history which contains at least the customers and the belonging time when the order was taken. We will skip the rest for now since it is not important to understand the problem.

What I want to do is, I want to predict when the customer will order next. So, I'm not interested in the amount of a certain product, just when will the customer order next. I have a real-life data set (so no pre-optimized stationary data set).

I thought its a good idea to use a time series. When I will plot the data visually, the x-axis will contain the time and the y-axis consists of 0 and 1 values, where 1 denotes the customer brought something and 0 he doesn’t.

What I want to understand is, if the Holt Winters approach is capable of dealing with zero/one values well? I found this article which covers my case but I'm not sure if the article uses that case just for explanation purposes or if it would work in practise as well. What they are doing is, they map the zero/one values relative to the average. I guess its because of preventing zero/one combinations.

It would be great if someone can share his/her experience with that.

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  • $\begingroup$ Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. $\endgroup$
    – Community Bot
    Commented Sep 21 at 14:10
  • $\begingroup$ i dont understand, this is a single problem with enough detailes for an adequate answer. $\endgroup$
    – raphael
    Commented Sep 22 at 9:45
  • $\begingroup$ I can tell you with a decent amount of certainty that you don't want to use Holt-Winters with binary values. That's not appropriate. It sounds like you have more of a model of "time until an event". That sounds more like a problem possibly in survival where the event is when someone orders rather than someone dying. But this part is not my field so my only advice is to stay away from using Holt-Winters. $\endgroup$
    – mlofton
    Commented Oct 13 at 1:40

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