The Forecast of The Number of Inbound Tourists and The Analysis of The Source Market During The Epidemic of Coronavirus Disease
Bibliographic record
Abstract
With the rapid development of economy, the competition of inbound tourism market is more and more fierce. The key point of sustainable development of inbound tourism is to ensure a certain number of tourists. Therefore, it is an important step to predict the number of inbound tourists and study the market of inbound tourists. As a leading tourism city in China, how to attract more tourists is not only related to the development of inbound tourism in Shanghai, but also provides some inspiration for other cities during the epidemic of Coronavirus Disease. In this paper, an improved grey markov (GM) model is used to predict the number of inbound tourists in Shanghai during the epidemic of Coronavirus Disease, and then the market changes of inbound tourists are studied by the deviation-share analysis method. Finally, the tim-scale characteristics and trends of inbound tourists in Shanghai are analyzed by ensemble empirical mode decomposition. GM (1,1) model is one of the most widely used grey dynamic prediction models in grey system theory, which is composed of a first order differential equation with a single variable. The initial value correction improves the gray GM (1,1) model, and introduces the center point triangle albino weight function in the state division to improve the Markova model. Comparing with the results of traditional GM (1,1), initial value modified GM (1,1) and traditional grey markov prediction models, the prediction effect of this model is verified to be better. These models are better than linear regression and time series. Deviation-share analysis explores the changes in the inbound tourist market, and the results show that from 2004 to 2017, the inbound tourist market in Shanghai developed faster than that in the whole country, with a more reasonable and competitive structure. In addition to Japan, the number of inbound tourists from each country to the whole country and Shanghai has increased and increased greatly. The time-scale characteristics and trends of inbound tourists in Shanghai are analyzed by ensemble empirical mode decomposition. The results show that: first, the total number of inbound tourists and the number of foreign tourists mainly change within 3 or 6 months, while that of Hong Kong, Macao and Taiwan fluctuates between high and low frequency. Second, the main cyclical fluctuations and no significant trend of the source countries. The fluctuation period of Japan, Thailand, Britain, France and Germany is 3 months; Macau is 3, 6, 12, 60, 180 months; Singapore is 3, 6, 180 months. Third, there is a clear trend and cycle fluctuations as a supplement to the source countries. The fluctuation periods in Hong Kong are 3, 6, 90 and 180 months; In Taiwan, Canada and Russia it is 3 , 6 months; In Indonesia, the United States, Italy and New Zealand it is 3, 6 and 12 months; In Malaysia it is 3, 180 months; In South Korea it is 3 ,45 months; In Australia it's four or seven months. Taiwan, Canada, Russia and New Zealand showing the most significant upward trend. From the above research results, specific Suggestions and strategies of market structure competition can be put forward to the inbound tourism industry in Shanghai according to the predicted number of inbound tourists in Shanghai, the structure of the source market and the cyclical fluctuation and trend of the source country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".