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I am working with daily binary time series forecast as follows:

  • The target : purchase decision (0: not purchase, 1 purchase
  • Features: day, weekday, promotion, holiday,....

The objective is trying to forecast that the day have purchase or not! So, What algorithms can be used to address this problem! I also research about markov chain , Survival analysis.. but i am not sure it can be applied?

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Although the nature of your data might present a time-series format, it looks like you can frame it as a usual classifier, building a tabular dataset based on the features you point out, something like:

day weekday promotion holiday purchase_target
1 Monday 0.5 True 0
2 Tuesday 0.3 False 0
3 Wednesday 0 False 1

You can begin with simple binary classifiers, like naive bayes classifier, logistic regressor... and aslso have a look at a comparisson of some others in this link.

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  • $\begingroup$ Thanks you! I also conduct this method. But the accuracy is not good! Any approach please suggest me! $\endgroup$
    – Sherry
    Sep 5 at 7:46
  • $\begingroup$ this method does not guarantee accuracy by itself; you could add some feature engineering trying to add new explainable features, but first, make sure your dataset is realiable and you build a baseline model to have a reference $\endgroup$
    – German C M
    Sep 5 at 9:55

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