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Add how fast each night is booking as a model input #103
Part of Pair 2: booking data into the model (Alex + Heather), under #88. Stretch for this week. Blocked by #88.
Problem
The model only knows the calendar, events and weather for a future night. It doesn't know how that night is actually selling, which is usually the strongest sign of extra demand: if a night is already 80% full a month out when similar nights are at 50%, it's underpriced.
Goal
The model sees how full each night is and how fast it's filling, both when learning from past nights and when pricing future ones.
Scope
This week (training and measurement):
New inputs for each night, as of the date the rate is set:
Use the same feature code for training and pricing.
Considerations
A rate depends on when it's set: a night 60 days out and the same night 2 days out need different inputs. Decide which "days ahead" to train on (e.g. one row per night per several lead times, or a fixed lead time).
"% sold so far" for future nights uses total rooms, not rooms − ROOS, because future nights' ROOS isn't imported (decision 0037).
If the latest upload is old, the inputs are wrong; use the upload date and flag when it's old.
Acceptance criteria
The new inputs are computed for training from the rebuild.
Activity