I wonder if anyone can help.

I have a set of data on event ticket sales. I have information on eventdate, location, capacity, cumulative sales, sales date, total sales.

I want to be able to build a model which takes a minimum input of The number of tickets currently sold and days until the event and it would return a value for predicted total tickets sold.

I think I need to use a time-series model or a Survival model, but I am unsure how structure my data and construct it in this instance.

Does anyone have any, tips/pointers/guidance?



If you are aware of the capacity of the event, you could model the probability that the accumulated sale of the event will reach 100%. That is, sample the cumulative sale of tickets per event and choose the probability distribution that best characterizes the time random variable (exponential, gamma, weibull). I recommend you to read the following article: Forecasting Event Ticket Sales (Suher,2008)

I regret the lateness of my answer, but there are similar questions in the forum (search: ticket sales), however yours summarizes quite a lot the information that is generally available.


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