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Research on the Tourist Volume Forecast of Scenic Spots Considering the Effect of Holidays-A Hybrid Prediction Method Based on Prophet-NNAR

LI Yong, LI Yunpeng   

  1. School of Business Administration, Capital University of Economics and Business, Beijing 100070
  • Received:2021-10-03 Revised:2022-03-15 Online:2022-06-25 Published:2022-07-29

LI Yong, LI Yunpeng. Research on the Tourist Volume Forecast of Scenic Spots Considering the Effect of Holidays-A Hybrid Prediction Method Based on Prophet-NNAR[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(6): 1537-1550.

Recently, incidents of tourists being stranded due to the overloaded reception of tourists in attractions are very common. Therefore, accurate and effective prediction of the tourist volume in attractions and rational allocation of resources has become a challenge for scenic spot managers. Because of the influence of external factors, such as holidays, the time series curve of tourist volume in attractions usually presents nonlinear characteristics, which undoubtedly increases the practical difficulty of accurately predicting the tourist volume. This study proposes a method for forecasting tourist volume in attractions that considers the effects of holidays, namely, the Prophet-neural network autoregressive (NNAR) hybrid forecasting method. First, the Prophet model, which considers the effects of holidays, is used to predict the original tourist volume of attractions. Then, the NNAR model is used to predict the residual part of the predicted value of the Prophet model. Finally, the two results are combined as the final prediction result of the Prophet-NNAR hybrid model. Taking the historical tourist volume data of Jiuzhaigou scenic spot (from January 1, 2013 to July 31, 2017) as the data source, the effectiveness of the Prophet-NNAR hybrid forecasting method is verified using the R software. Results show that the Prophet-NNAR hybrid forecasting method is effective. The prediction performance of the Prophet-NNAR hybrid forecasting method is not only better than that of single-model methods (i.e., Prophet model, Prophet model that does not consider the effects of holidays, and NNAR model) but also stronger than the seasonal autoregressive integrated moving average and exponential smoothing models. Moreover, the combined results of the Diebold-Mariano test can confirm that the superiority of the Prophet-NNAR hybrid forecasting method over the other methods is statistically significant.

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