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How is the Power of the Baidu Index for Forecasting Hotel Guest Arrivals? –A Case Study of Guilin

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Data Science (ICPCSEE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1629))

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Abstract

The prediction of the Baidu index for tourism demand has been increasingly focused on by scholars. However, few studies have evaluated the predictive power of the Baidu index for hotel guest arrivals in fine granularity at the micro level. Taking Guilin as a case study, we use the OLS regression method to quantitatively investigate the forecasting power of the Baidu index for daily hotel guest arrivals and to comprehensively evaluate the performance of the forecasting model and to optimize the forecasting model by deeply mining the hidden characteristics of tourism flow in a special case study. The contributions of this paper mainly have threefold: first, to the best of our knowledge, based on the actual full-example of daily hotel guest check-in data in fine granularity, we evaluated the predictive power of the Baidu index by comparison of 5 forecasting models for the first time. Second, we proposed two metrics for forecasting: the trend forecasting index and the forecasting stability index. Finally, we introduce a kind of punishment strategy to optimize forecasting models based on the potential pattern of research objects.

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Acknowledgement

We would like to extend our gratitude to the Guilin Tourism and Development Committee for the support of this study. We acknowledge the anonymous reviewers and the editors of Journal. This study is supported by the research on key technology of tourism destination safety warning and its application demonstration granted by No. Guike AB17195028, technology development of tourist safety warning system for smart scenic spot and virtual spatiotemporal reconstruction of special culture and its application demonstration granted by No. 20170220, and research on sustainable utility technology integration of Longji terrace landscape resources and tourism industry demonstration granted by No. 20180102-2, Guangxi natural science fund by No. 2018GXNSFAA138209.

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Authors and Affiliations

  1. Guilin University of Technology, No. 12, Jiangan Street, Qixing District, Guilin, 541004, Guangxi, China

    Haitao Yu, Le Liu, Zhongjun Wu & Yajun Jiang

  2. Guangxi Tourism Industry Research Institute, No. 12, Jiangan Street, Qixing District, Guilin, 541004, Guangxi, China

    Haitao Yu

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  1. Haitao Yu
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  2. Le Liu
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  3. Zhongjun Wu
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  4. Yajun Jiang
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Corresponding author

Correspondence to Yajun Jiang .

Editor information

Editors and Affiliations

  1. Southwest Petroleum University, Chengdu, China

    Yang Wang

  2. University of Electronic Science and Technology of China, Chengdu, China

    Guobin Zhu

  3. Harbin Engineering University, Harbin, China

    Qilong Han

  4. Southwest Petroleum University, Chengdu, China

    Liehui Zhang

  5. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  6. National Academy of Guo Ding Institute of Data Sciences, Beijing, China

    Zeguang Lu

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Yu, H., Liu, L., Wu, Z., Jiang, Y. (2022). How is the Power of the Baidu Index for Forecasting Hotel Guest Arrivals? –A Case Study of Guilin. In: Wang, Y., Zhu, G., Han, Q., Zhang, L., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1629. Springer, Singapore. https://doi.org/10.1007/978-981-19-5209-8_13

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  • DOI: https://doi.org/10.1007/978-981-19-5209-8_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5208-1

  • Online ISBN: 978-981-19-5209-8

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