I looked around to see if there was a similar question, but couldn´t find one. I apologize if there is one and I missed it.
I have the amount of ticket sales per day for 10 different events. The amount of days is different for each event, as well as the number of ticket sold per event. Almost all events show a spike in ticket sales at the start of the selling period.
I want to be able to forecast the (multiple) ticket sales. I do not want to forecast each event individually, but one model that fits (more or less) all. I tried combining the data and use auto.arima (in R) on the combined data, but I have the impression the spikes influence too much the result.
What would be the best way to proceed? How can I take into account all events?
tt <- list( c(6,3,532,162,54,69,84,43,27,42,54,44,27,21,45,34,26,32,15,27,17,14,5,19,6, 21,12,10,27,30,22,19,13,22,41,14,211,22,4,27,28,13,15,19,5,13,8,23,33,25,16,41, 18,36,24,26,25,27,42,37,24,32,32,32,34,49,33,72,28,73), c(12,4,4,14,8,15,2472,2144,1031,462,214,340,286,263,244,196,178,73,152,216,487,350,311), c(16,2,11845,2374,1492,2074,1654,1303,1090,978,912,542,968,832,1097,724,654, 645,403,482,12,5740,988,50,8,843,137,6,754,585,111,194,247,162,248,111,122,201, 208,166,49,2,78,168,446,106,78,1))