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I have a problem a need your suggestion , I am working in a retail data , and want to predict the behavior of the customer , the data contains information about the customer who visits the shopping center, it only contains the customer unique identifier and the customer visits for 143 weeks so for each record I have visitor Id and visits , the visits contains numbers such as 1 5 30 ..etc , 1 is the first day of the first week which is Monday then 5 means Friday , then 30 means Tuesday for the fourth week and etc , what I am trying to do is to predict when the first day of the visit for the next 144th week can you please help me in this this is the data format visitor_id visits
1 30 84 126 135 137 179 242 342 426 456 460 462 483 594 600 604 704 723 744 787 804 886 924 928 946 954
2 24 53 75 134 158 192 194 211 213 238 251 305 404 418 458 476 493 571 619 731 739 759 761 847 883 943 962 981 983

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Firstly, since your series is consists of visits in the form of 1 5 30, therefore applying time-series modeling techniques such as ARIMA, ETS, etc won't be of much use as these require a continuous series to be inputted and their output is a forecast for the very first day of 144th week which is useless in this situation.

Secondly, The following are a couple of my suggestions:

  1. Generate new features from the data for the number of visits, visits on various days of the week, longest break between two visits, average number of visits per week and much more. Think about the features that may be useful for a human forecaster to make this sort of a prediction!
  2. For this kind of a data, It will be better to employ Multinomial Logistic Regression or Neural Network models where the input layer will consist of all the generated attributes and the output layer will be a set of probabilities for the 7 days of the 144th week.

The day with the highest probability for each visitor_id will the likeliest day for a future visit in the 144th week.

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