Ideas for dealing with bounding time series forecasting?
My time series which are sales were limited for certain days in the past. For example, there was a capacity constraint on a day e.g. 1000 orders hence the orders reached this limit while they could had exceed it e.g. 1200 orders.
However, the tricky part of those capacity limits is that they were not constant over time. For example, the business saw that they were reaching full capacity so they took measures to increase it for a bit for future days until maximum capacity was reached again.
Now, I want to forecast the same time series but the capacity limit does not exist anymore.
Furthermore, I give a representative simple example:
y_variable___ = [ 5, 9, 6, 9, 9, 15, 9 , 14, 15, 25, 20, 18, 26]
max_capacity = [ 9, 9, 9, 9, 9, 15, 15, 15, 15, 25, 25, 25, 29]
So in the cases that the sales are reaching maximum capacity I probably have some unforeseen sales, or in other words I could had more sales.
Any ideas on how to deal with that?