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I have temporal data regarding the number of customers who requested a specific service in a given period (month and year). Below is a small excerpt from the dataset:

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  • Month-year: month and year when the service has been requested/offered
  • Service Description: the tipology of the service request by the customer
  • occurences: how many times the customers in that period requested that service

I have monthly data from 2003 to 2020 and I would like to carry out a predictive analysis to predict the number of events from 2021 to 2023 and also predict the type of services. For the first I know that I have to face the problem using the analysis of the time series, I have doubts about the second part ... how to predict the type of service in addition to the number of requests? Can you give me some suggestions?

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You could have one model for each type of service , which would predict the number of events in a given month and year based on history.

You could also jointly model them using a neural network to approximate the distribution or gaussian process.

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