Forecast Trend Adjustment Analysis Report (Cloud)
The Forecast Trend Adjustment Analysis report is a diagnostic tool within the PMS Revenue Management module. It allows hoteliers to evaluate the accuracy of different forecast adjustment models by comparing actual historical performance against simulated estimates generated by each trend logic. This report helps determine which forecast trend adjustment setting—None, Historical, or Budget—produces the most accurate results for a given property.
Path: Revenue Management Live! > Reports
Date Range & Adjustment Types
Report Period: The report can be run for any past or future date range (e.g., 06/30/25 – 07/14/25).
Adjustment Models:
- Estimate #1: No adjustment (raw data).
- Estimate #2: Historical Year-Over-Year.
- Estimate #3: Budget-based projection.
Report Structure
The report is split into four main segments across each row of data.
If run for past dates, the Actual column will be populated, enabling comparison and accuracy evaluation.
Purpose and Use
This report is designed to test and compare the forecasting accuracy of the three available adjustment logics:
- By reviewing past performance (i.e., where actual data is available), users can evaluate how well each forecast logic matched reality.
- This can help hoteliers determine the best-fit trend adjustment strategy for their property.
For example:
"For the period of July 1–15, Estimate #2 (Historical) was off by 63 rooms total, Estimate #1 (None) was off by 203 total, and Estimate #3 (Budget) was off by 143 total. Therefore, the Historical model produced the most accurate results for that range."
This information guides users to:
- Adjust their forecast logic settings if needed.
- Understand the strengths and weaknesses of each model for specific periods.
Gain insight into how seasonal, event-driven, or unusual demand patterns are reflected across adjustment methods.
Best Practices
Use longer periods (e.g., a month or quarter) for more meaningful variance analysis.
Re-run the report for multiple seasons to evaluate which model performs better in each.
Understand that all estimates are approximations—the goal is to minimize, not eliminate, forecast error.
Reassess the adjustment setting periodically as business conditions and data quality evolve.
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