How should I deal with features with time related values in Bayesian network? I would like to apply Bayesian network on some data. However, some of the variables are related to time. 
E.g.  Number of time he/she visit library.
As the value can be defined as Total number of this person visits, Average weekly number of he/she visit, Daily number of visits, or even Number of visits in the last 3 days etc...
I do not want to include all these different variations in my network
Then how should I treat feature like this properly?
Many thanks for your help
 A: I am afraid you won't like this answer, but it depends on what you want to do. 
Non-Time-Series-Modelling-Point of View
Here is an example: Suppose you want to predict whether a certain person is visiting the library at the next day. The starting point for your data is hence the data-id + person_id + label (visit=true/false). Now the question is which predictors are needed. Can you tell me which of this predictors is meaningful or not (without performing any modelling) ? 


*

*average-number-of-visits per week: Persons visiting the library more often have a higher chance of visiting the next day

*number-of-visits-in-the-last/days/hours: Some people go one only once per week to the library, but not always on the same day. So depending on the total-visit-count per week + whether the customer has visited the library recently, one could say whether the same person will come in again the next day.

*daily-number-of-visits: Every day multiple visits ? Or only multiple visits on the one weekday where the person comes in ?


and so on ... I think you get the idea. I would try to generate a good amount of predictors and then apply subset selection techniques to find the best combination (yes, I am a machine learner).
Time-Series-Modelling-Point of View
I admit I do not know that much about time-series analysis. I can only imagine that drawing one timeline per customer (e.g. in days or hours on the time/x-axis + plus a peak everytime the customer visits) and classifying this timelines (in context of the example in the first section) might help.
PS: I am aware that this answer is not as complete as it should be. However, I hope to stimulate the discussion that way so one can come up with the true answer.
