Table of Contents

1. Problem Definition & Methods
2. Contribution
   1. TODO & Questions & Further Reading

This work mainly focus the hotel.

Problem Definition & Methods

1. Objective: max revenue
2. Main components
   1. get reasonable base rate
      1. average value of its historical sales
   2. occupancy prediction
      1. model:
         1. input: data from previous 28day
         2. output: occupancy for the next 7 days
      2. Metric: MAPE
   3. dynamic pricing system to model human expertise
      1. Features
         1. base rate
         2. predicted occupancy
         3. other relating factors
      2. data
         1. Assumption: We assume that the prices of such samples are adjusted by hotel managers to increase the revenue and the pricing strategy considering a variety of situational factors can be transferred to other hotels.
         2. How: we select hotel samples of which the revenues in the period increase compared with their revenues of previous periods, eg., last week, last month or last year.

Contribution

1. occupancy prediciton model
2. dynamic pricing model

TODO & Questions & Further Reading

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  2023 - A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization
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