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I have 2 datasets from 2013-2017 for each day. a) Revenue generated by Locations and date. b) Holiday name and date

I would like to know how each holiday is impacting the revenue by location. I am confused about what method to use! Should 3-Sigma rule or Bollinger plot be enough or should I try spline regression or something else? FYI I am using python for this analysis.

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  • $\begingroup$ Welcome to the community @Rohan. The solution to your problem has a wide scope and can cause the question to be closed. Please try to elaborate on a specific problem rather than the overall concept. $\endgroup$
    – thanatoz
    Commented Dec 2, 2018 at 20:03
  • $\begingroup$ I am trying to forecast the revenue by location and day. I am trying to use Holidays as one of the predictor variable. So I am trying to give some weight to each Holiday depending upon how they have impacted the revenue in the past. $\endgroup$
    – Rohan Singh Dhaka
    Commented Dec 2, 2018 at 20:08

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It might be not the best approach, but without knowing the data its hard to be precise. If you have no ideas where to start, I can suggest trying this things first.

  1. If you want to get the "impact" of the holiday, try to one-hot encode "holiday" variable, and use some simple model that is interpretable. You need to undestand how each holiday-feature impacts the revenue, maybe linear regressiong is a good way to start and move on to more advanced algo`s.
  2. You can try to play with Facebook`s Prophet - its an package for timeseries predictions, wich shows the daily impacts by default. Here is the link: https://research.fb.com/prophet-forecasting-at-scale/ . And link to python package: https://github.com/facebook/prophet

Additional post about Propet and transactios forecasting: https://www.kaggle.com/armamut/predicting-transactions-fb-prophet-tutorial

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