Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures
Bibliographic record
Abstract
Research in hotel revenue management system design has not paid much attention to the demand forecasting side of the system. And the research that has examined forecasting has tended to focus on the comparison of specific forecaster methodologies, as opposed to prioritizing how a total system should be parameterized: how far in the future should projections be, how much data to use to update each specific parameter, which measure of forecast error to use, and how long to freeze each parameter/forecast before updating. This paper fills this prioritization void by utilizing a full-functionality hotel reservation system simulation validated by the revenue management staff of a major hotel chain as the basis for running screening experiments on an exhaustive set of forecaster parameters with regards to their impact on bottom-line system performance (average nightly net revenue, where net revenue refers to total room rate receipts minus an overbooking per person penalty that estimates the discounted lost sales of future revenues). A screening experiment is run for each general type of operating environment (demand intensity, degree of market segment differentiation) that a property might face. We find that only two parameters are significant: the final combined forecast horizon length and how long that final forecast is frozen before updating. We find that these two factors interact in a negative fashion to influence net revenue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".