Time series prediction: need help using series with different periods of days
Based on what I understand after reading your question, I would approach this problem in the following way.

For each day, find how far out the event is from that day. The max
value for this number is 46 in 2016, 77 in 2017 etc. Scale this value
by the max day.
Use the above variable, along with day of the month, day of the week
etc as extraneous variable
Additionally, use lag information from ticket sales. You can try one
day lag, one week lag etc.
You would be able to generate all this data from the sale start until
end.
Use the generated variables as predictor for each day and use ticket
sales as target variable and generate a machine learning model
instead of forecasting.
Use the machine learning model along with generated variables to predict future sales.
There's this event that my organization runs, and we have the ticket sales historic data from 2016, 2017, 2018. This data contains the quantity of tickets selled by day, considering all the sales period.
To the 2019 edition of this event, I was asked to make a prediction of the quantity of tickets selled by day, considering all the sales period, sort of to guide us through this period, meaning we would have the information if we are above or below the expected sales average.  
The problem is that the historic data has a different size of sales period in days:
In 2016, the total sales period was 46 days.
In 2017, 77 days.
In 2018, 113 days.
In 2019 we are planning 85 days. So how do I  ajust those historical data, in a logic/statistical way, so I could use them as inputs to a statistical predictive model (such as ARIMA model)?
Also, I'm planning to do this on Python, so if you have any suggestions about that, I would love to hear them too!
Thank you!