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Developing and testing the efficacy of a novel forecasting methodology: Theory and evidence from China

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  • Yuhong Yang
  • Tarik Dogru
  • Chao Liang
  • Jianqiong Wang
  • Pengfei Xu

Abstract

Numerous methodologies have been offered to forecast tourism demand; however, accurate forecasting has been a major challenge for policymakers despite its critical importance for tourism planning. Therefore, we propose and test a novel forecasting methodology that combines principal component analysis (PCA) and long short-term memory (LSTM) network, along with the Baidu index, to forecast daily tourist arrivals for a popular tourist attraction in China. Word2Vec, a software tool launched by Google, is used to improve the coverage and accuracy of search keywords in the construction of the Baidu indexes. Before training the LSTM network, PCA is used to reduce noise and optimize the data. Considering the study’s timeframe, the impact of COVID-19 pandemic has also been assessed. The efficacy of the proposed forecasting methodology is verified, and the results show that the PCA-LSTM model outperforms other models in terms of prediction accuracy and stability. Theoretical and practical implications are discussed.

Suggested Citation

  • Yuhong Yang & Tarik Dogru & Chao Liang & Jianqiong Wang & Pengfei Xu, 2024. "Developing and testing the efficacy of a novel forecasting methodology: Theory and evidence from China," Tourism Economics, , vol. 30(8), pages 2043-2069, December.
    • Handle: RePEc:sae:toueco:v:30:y:2024:i:8:p:2043-2069
    DOI: 10.1177/13548166241248866
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    References listed on IDEAS

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    1. Gunter, Ulrich & Önder, Irem, 2015. "Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data," Tourism Management, Elsevier, vol. 46(C), pages 123-135.
    2. Claudio Calero & Lindsay W Turner, 2020. "Regional economic development and tourism: A literature review to highlight future directions for regional tourism research," Tourism Economics, , vol. 26(1), pages 3-26, February.
    3. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    4. Issa Atoum, 2019. "Scaled Pearson’s Correlation Coefficient for Evaluating Text Similarity Measures," Modern Applied Science, Canadian Center of Science and Education, vol. 13(10), pages 1-26, October.
    5. Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
    6. Jorge V Pérez-Rodríguez & Heiko Rachinger & María Santana-Gallego, 2022. "Does tourism promote economic growth? A fractionally integrated heterogeneous panel data analysis," Tourism Economics, , vol. 28(5), pages 1355-1376, August.
    7. Tarik Dogru & Umit Bulut & Ercan Sirakaya-Turk, 2021. "Modeling tourism demand: Theoretical and empirical considerations for future research," Tourism Economics, , vol. 27(4), pages 874-889, June.
    8. Wenxing Lu & Jieyu Jin & Binyou Wang & Keqing Li & Changyong Liang & Junfeng Dong & Shuping Zhao, 2020. "Intelligence in Tourist Destinations Management: Improved Attention-based Gated Recurrent Unit Model for Accurate Tourist Flow Forecasting," Sustainability, MDPI, vol. 12(4), pages 1-20, February.
    9. Song, Haiyan & Hyndman, Rob J., 2011. "Tourism forecasting: An introduction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 817-821, July.
    10. Emrah Koçak & Tarik Dogru & Khurram Shehzad & Umit Bulut, 2023. "The economic implications of the COVID-19 outbreak on tourism industry: Empirical evidence from Turkey," Tourism Economics, , vol. 29(3), pages 742-758, May.
    11. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    12. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
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