The Forecast of The Number of Inbound Tourists and The Analysis of The Source Market During The Epidemic of Coronavirus Disease
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».