Demand Forecasting Model To Reduce The Mean Absolute Percentage Error By Applying Seasonal Breakdown Tools In A Sme In The Tourism Sector
Bibliographic record
Abstract
The research work is based on the analysis of demand in a tourism company using mathematical models.The methodology design presents a correlational and descriptive scope where the company's sales are collected to calculate the mean absolute percentage error in demand.With the help of machine learning tools, a predictive analysis will be carried out to estimate the sales for the following year, seeking to reduce the error using one of the selected mathematical models, calculate the necessary sales force, and thereby reduce the economic impact equivalent to $16 789,02.The MAPE (Mean Absolute Percentage Error) in the tourism sector is 12,03%.Through calculations using Python and RISK, a value of 15,36% was obtained, reducing the MAPE by 4,24% compared to the year 2022.The Systematic Review of the Literature allows us to showcase the tools that can be developed in similar or atypical scenarios.The choice will depend on the behaviour pattern or trend.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".