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Daily tourism forecasting through a novel method based on principal component analysis, grey wolf optimizer, and extreme learning machine

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  • Chuan Zhang
  • Ao‐Yun Hu
  • Yu‐Xin Tian

Abstract

Accurate forecasting tourism demand is crucial for improving the economic benefits of tourist attractions, but it is a challenging task. In this paper, we propose an effective daily tourism forecast model, principal component analysis‐grey wolf optimizer‐extreme learning machine (PCA‐GWO‐ELM), based on Baidu index data, holiday data, and weather data. Our model uses PCA to reduce the dimensionality of the data and employs the GWO to optimize the number of neural networks in the hidden layer of the ELM model, improving its forecast performance. We conduct an empirical study using the collected tourist data of Mount Siguniang. The results show that the proposed hybrid forecasting model outperforms other models in daily tourism demand forecasting, making it a potential candidate method for practitioners and researchers studying tourism demand forecasting.

Suggested Citation

  • Chuan Zhang & Ao‐Yun Hu & Yu‐Xin Tian, 2023. "Daily tourism forecasting through a novel method based on principal component analysis, grey wolf optimizer, and extreme learning machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2121-2138, December.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:8:p:2121-2138
    DOI: 10.1002/for.3007
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    References listed on IDEAS

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