Tourists Forecast Lanzhou Based on the Baolan High-Speed Railway by the Arima Model-Reference-Cited by-同舟云学术

Tourists Forecast Lanzhou Based on the Baolan High-Speed Railway by the Arima Model

Published:2020-01-01 Issue:1 Volume:5 Page:55-60
ISSN:2444-8656
Container-title:Applied Mathematics and Nonlinear Sciences
language:en
Short-container-title:

Author:

Li Lijun¹,Wang Yaru¹,Li Xiaoyue²

Affiliation:

1. School of Economic & Management , Shijiazhuang Tiedao University , Shijiazhuang 050043 , China

2. Fisher College of Business , Ohio State University , Columbus OH43210 , USA

Abstract

Abstract According to the analysis from the number of tourists who went to Lanzhou during 2009–2019, the ARIMA model of the number of tourists to Lanzhou was established. The results show that the AR(3) model is used to predict the number of tourists who traveled to Lanzhou during 2009–2019. The average relative error between the predicted value and the actual value is 1.03%, which can be used to predict and analyze the number of tourists in Lanzhou in the future.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Link

https://www.sciendo.com/pdf/10.2478/amns.2020.1.00006

Reference8 articles.

1. Li Wei, (2019), Forecast of the number of tourists based on the ARIMA model, Jiangxi University of Finance and Economics.

2. Kong Chaoli, (2019), Hainan monthly tourist population forecast based on ARIMA model, China Science and Technology Information, 05, 76–78.

3. Liu Ruoyu, Liu Libo, (2019), Analysis and prediction of the number of visitors based on the ARIMA model, Computer and Telecom, Z1, 1–4.

4. Liu Yanyu, Long Jinping, (2016), An empirical analysis of the number of tourists in Jiangkou County based on the ARIMA model, Statistics and Management, 06, 36–37.

5. Chen Peng, Wu Ling, Song Hui, (2012), Forecast of the number of inbound tourists in Anhui Province based on ARIMA model, Journal of Anhui Agricultural University (Social Science Edition), 21, 32–35+116.

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