Final paper reporttourist prediction.docx

1、Final paper reporttourist predictionStudy on the Prediction of Tourist Attractions based on the Baidu Index: A Case Study of the Forbidden CityCourse : English for tourism1：Number: 51153902033 Name: 杨舒婷 Major：Geography2：Number: 51150601169 Name: 张教根 Major： Math 3：Number: 51150601151 Name: 胡夏朕 Major：

2、 Math 4：Number: 51150601168 Name: 袁东 Major： Math 5：Number: 51150601194 Name: 涂欢 Major： Math 6：Number: 51153903030 Name: 朱小静 Major： Ecology Adviser： 严文庆 2016年6月CatalogAbstract：.11:Introduction.22:Research status review.33: Empirical analysis. .5 3.1:The selection of Baidu keyword with the data.5 3.2:

3、Relationship between Web search data and the actual data.6 3.3 Establishment of prediction model and analysis.9 3.3.1:establishment of prediction ARMA model and analysis.9 3.3.2:Establishment and prediction analysis of autoregressive distributed lag model.114:Conclusion .125:Refercences.14Study on t

4、he Prediction of Tourist Attractions based on the Baidu Index: A Case Study of the Forbidden CityAbstract Tourists overflowing during the “Golden Week” is not an uncommon situation in China today. Predicting tourist flows is significant for tourist attractions management and planning. Internet searc

5、h records can reflect concerns and interests of potential tourists, and provide a large volume of unstructured or semi-structured data for studying tourism economic behavior. This paper proposes a novel approach for predicting tourist flow based on the Baidu Index, which provides search history cont

6、aining different keywords on a daily basis dating back to 2006. In this paper, we conduct a case study using search data related to the Forbidden City from the Baidu Index and statistical data of tourist flows in the Forbidden City. Firstly, using the econometric co-integration theory and Granger ca

7、usality analysis this paper finds relationships between the internet search data and the actual tourist flow, which indicates a positive correlation between them. Then, this paper compares analysis results obtained by two kinds of predictive models with (ARDL) or without (ARMA) considering Baidu Ind

8、ex. The ARDL model improves the prediction accuracy of the training sample by 12.4%, and the testing sample by 14.5%. Our approach can predict the number of daily visitors of the Forbidden City using the one or two days lagging data from the Baidu Index, while the previous forecasting method require

9、s data of a much longer period. In conclusion, it improves the timeliness and accuracy of the prediction, and provides tourism management departments with better evidence for decision-making. Keywords: Baidu Index; tourist attractions; co-integration; autoregressive moving average model (ARMA); auto

10、regressive distributed lag model (ARDL)1. Introduction With the booming development of tourism in China, there is a large increase in number of both domestic and foreign visitors to some famous tourist city and scenic spots. Especially during Golden Week, such as National Day, Labor Day, several tou

11、rist attractions are overcrowded and far beyond the carrying capacity of the scenic area. There is a list of top 10 spots we should never visit during Golden Week from Net ease News, including the Forbidden City, Huangshan Mountain, Dali-Lijiang, Jiuzhaigou National Park, etc. There are even some tr

12、agedies caused by crowd. For example, in 2012, during the National Day holiday, the killing caused by thousands of stranded visitors in Huashan Mountain created a great stir in our country. In order to avoid tragedy and provide a better travel environment, a more accurate prediction of tourist flows

13、 combined with the carrying capacity of the scenic spot is particularly urgent and significant for making timely arrangements and reasonable planning. Therefore, the key problem of our research is how to predict visitor numbers of scenic spots more scientifically. At present, most existing prediction methods rely on well-stru