Mathematical modeling and forecasting of seasonal characteristics of tourist flow
Bibliographic record
Abstract
The article describes the flow of tourists to the Republic of Uzbekistan and the methods of analysis and forecasting based on econometric modeling of the development of the process based on its seasonal characteristics. Econometric modeling methods developed by foreign and local scientists were analyzed and divided into groups to analyze the process of changing the flow of tourists and predict the future number. Among them, the additive model in the group of time series reflecting the seasonality of tourist flow was found to meet the conditions. Based on the data obtained in the quarters for 2014-2018, the values of the trend (T), seasonal (S), and random (E) components of the time series were calculated step by step, and an additive model of the process was developed. Based on the developed model, the forecast values of tourist flow for the next quarter were determined, and the deviation from the actual value of the theoretical result was 20%, and the occurrence of this deviation was clarified. Forecasts of changes in the statistics of the tourism sector have been developed. The article describes the methods of analysis and forecasting of tourist flows and seasonality of the Republic of Uzbekistan based on econometric modeling.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".