A deep learning model based on multi-source data for daily tourist volume forecasting

References

• Álvarez-Díaz, M., & Rosselló-Nadal, J. (2010). Forecasting British tourist arrivals in the Balearic Islands using meteorological variables. Tourism Economics, 16(1), 153–168. https://doi.org/10.5367/000000010790872079
   Web of Science ®Google Scholar
• Amelung, B., Nicholls, S., & Viner, D. (2007). Implications of global climate change for tourism flows and seasonality. Journal of Travel Research, 45(3), 285–296. https://doi.org/10.1177/0047287506295937
   Google Scholar
• Bangwayo-Skeete, P. F., & Skeete, R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management, 46, 454–464. https://doi.org/10.1016/j.tourman.2014.07.014
   Web of Science ®Google Scholar
• Bausch, T., Gartner, W. C., & Humpe, A. (2021). How weather conditions affect guest arrivals and duration of stay: An alpine destination case. International Journal of Tourism Research, 23(6), 1006–1026. https://doi.org/10.1002/jtr.2459
   Web of Science ®Google Scholar
• Becken, S. (2013). Measuring the effect of weather on tourism. Journal of Travel Research, 52(2), 156–167. https://doi.org/10.1177/0047287512461569
   Web of Science ®Google Scholar
• Bi, J. W., Li, H., & Fan, Z.-P. (2021). Tourism demand forecasting with time series imaging: A deep learning model. Annals of Tourism Research, 90, 103255. https://doi.org/10.1016/j.annals.2021.103255
   Web of Science ®Google Scholar
• Bi, J. W., Liu, Y., & Li, H. (2020). Daily tourism volume forecasting for tourist attractions. Annals of Tourism Research, 83, 102923. https://doi.org/10.1016/j.annals.2020.102923
   Web of Science ®Google Scholar
• Białek, J., Bujalski, W., Wojdan, K., Guzek, M., & Kurek, T. (2022). Dataset level explanation of heat demand forecasting ANN with SHAP. Energy, 261, 125075. https://doi.org/10.1016/j.energy.2022.125075
   Web of Science ®Google Scholar
• Falk, M. (2013). Impact of long-term weather on domestic and foreign winter tourism demand. International Journal of Tourism Research, 15(1), 1–17. https://doi.org/10.1002/jtr.865
   Web of Science ®Google Scholar
• Falk, M. (2014). Impact of weather conditions on tourism demand in the peak summer season over the last 50years. Tourism Management Perspectives, 9, 24–35. https://doi.org/10.1016/j.tmp.2013.11.001
   Google Scholar
• Gómez Martín, M. B. (2005). Weather, climate and tourism a geographical perspective. Annals of Tourism Research, 32(3), 571–591. https://doi.org/10.1016/j.annals.2004.08.004
   Web of Science ®Google Scholar
• Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. ArXiv preprint arXiv:1207.0580.
   Google Scholar
• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
   PubMed Web of Science ®Google Scholar
• Huang, B., & Hao, H. (2021). A novel two-step procedure for tourism demand forecasting. Current Issues in Tourism, 24(9), 1199–1210. https://doi.org/10.1080/13683500.2020.1770705
   Web of Science ®Google Scholar
• Huang, L., & Zheng, W. (2021). Novel deep learning approach for forecasting daily hotel demand with agglomeration effect. International Journal of Hospitality Management, 98, 103038. https://doi.org/10.1016/j.ijhm.2021.103038
   Web of Science ®Google Scholar
• Jiao, E. X., & Chen, J. L. (2019). Tourism forecasting: A review of methodological developments over the last decade. Tourism Economics, 25(3), 469–492. https://doi.org/10.1177/1354816618812588
   Web of Science ®Google Scholar
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM , 60(6), 84–90. https://doi.org/10.1145/3065386
   Web of Science ®Google Scholar
• Kulshrestha, A., Krishnaswamy, V., & Sharma, M. (2020). Bayesian BILSTM approach for tourism demand forecasting. Annals of Tourism Research, 83, 102925. https://doi.org/10.1016/j.annals.2020.102925
   Web of Science ®Google Scholar
• Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75, 410–423. https://doi.org/10.1016/j.annals.2019.01.014
   Web of Science ®Google Scholar
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Li, C., Zheng, W., & Ge, P. (2022). Tourism demand forecasting with spatiotemporal features. Annals of Tourism Research, 94, 103384. https://doi.org/10.1016/j.annals.2022.103384
   Web of Science ®Google Scholar
• Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism Management, 68, 301–323. https://doi.org/10.1016/j.tourman.2018.03.009
   Web of Science ®Google Scholar
• Li, K., Lu, W., Liang, C., & Wang, B. (2019). Intelligence in tourism management: A hybrid FOA-BP method on daily tourism demand forecasting with web search data. Mathematics, 7(6), 531. https://doi.org/10.3390/math7060531
   Web of Science ®Google Scholar
• Li, S., Chen, T., Wang, L., & Ming, C. (2018). Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index. Tourism Management, 68, 116–126. https://doi.org/10.1016/j.tourman.2018.03.006
   Web of Science ®Google Scholar
• Li, X., & Law, R. (2020). Forecasting tourism demand with decomposed search cycles. Journal of Travel Research, 59(1), 52–68. https://doi.org/10.1177/0047287518824158
   Web of Science ®Google Scholar
• Li, X., Pan, B., Law, R., & Huang, X. (2017). Forecasting tourism demand with composite search index. Tourism Management, 59, 57–66. https://doi.org/10.1016/j.tourman.2016.07.005
   Web of Science ®Google Scholar
• Li, Y., Li, Y., Li, J., Ma, & Gao, P. (2022). Tourism demand forecasting from the perspective of mobility: A brand-new predictive variable generated from intercity population mobility big data. Asia Pacific Journal of Tourism Research, 27(5), 526–546. https://doi.org/10.1080/10941665.2022.2091941
   Web of Science ®Google Scholar
• Liu, Y., Feng, G., Chin, K.-S., Sun, S., & Wang, S. (2022). Daily tourism demand forecasting: The impact of complex seasonal patterns and holiday effects. Current Issues in Tourism, 1–20. https://doi.org/10.1080/13683500.2022.2060067
   Web of Science ®Google Scholar
• Lundberg, S. M., & Lee, S.-I. (2017). A unified Approach to Interpreting Model Predictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 30, pp. 4765–4774). Curran Associates, Inc.
   Google Scholar
• Mangalathu, S., Hwang, S.-H., & Jeon, J.-S. (2020). Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Engineering Structures, 219, 110927. https://doi.org/10.1016/j.engstruct.2020.110927
   Web of Science ®Google Scholar
• Meyer, D., & Dewar, K. (1999). A new tool for investigating the effect of weather on visitor numbers. Tourism Analysis, 4(3–4), 145–155.
   Google Scholar
• Ni, T., Wang, L., Zhang, P., Wang, B., & Li, W. (2021). Daily tourist flow forecasting using SPCA and CNN-LSTM neural network. Concurrency and Computation: Practice and Experience, 33), https://doi.org/10.1002/cpe.5980
   Web of Science ®Google Scholar
• Niu, Z., Zhong, G., & Yu, H. (2021). A review on the attention mechanism of deep learning. Neurocomputing, 452, 48–62. https://doi.org/10.1016/j.neucom.2021.03.091
   Web of Science ®Google Scholar
• Ooi, N., Duke, E., & O’Leary, J. (2018). Tourism in changing natural environments. Tourism Geographies, 20(2), 193–201. https://doi.org/10.1080/14616688.2018.1440418
   Web of Science ®Google Scholar
• Palmer, A., José Montaño, J., & Sesé, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781–790. https://doi.org/10.1016/j.tourman.2005.05.006
   Web of Science ®Google Scholar
• Pan, B., & Yang, Y. (2017). Forecasting destination weekly hotel occupancy with big data. Journal of Travel Research, 56(7), 957–970. https://doi.org/10.1177/0047287516669050
   Web of Science ®Google Scholar
• Park, E., Park, J., & Hu, M. (2021). Tourism demand forecasting with online news data mining. Annals of Tourism Research, 90, 103273. https://doi.org/10.1016/j.annals.2021.103273
   Web of Science ®Google Scholar
• Qiu, X. (2020). Neural Network and Deep Learning. https://nndl.github.io/. China Machine Press
   Google Scholar
• Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). The performance of LSTM and BiLSTM in forecasting time series IEEE International Conference on Big Data (Big Data), 2019 (pp. 3285–3292). doi:10.1109/BigData47090.2019.9005997.
   Google Scholar
• Song, H., & Hyndman, R. J. (2011). Tourism forecasting: An introduction. International Journal of Forecasting, 27(3), 817–821. https://doi.org/10.1016/j.ijforecast.2011.03.001
   Web of Science ®Google Scholar
• Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—A review of recent research. Tourism Management, 29(2), 203–220. https://doi.org/10.1016/j.tourman.2007.07.016
   Web of Science ®Google Scholar
• Song, H., Qiu, R. T. R., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the annals of tourism research curated collection on tourism demand forecasting. Annals of Tourism Research, 75, 338–362. https://doi.org/10.1016/j.annals.2018.12.001
   Web of Science ®Google Scholar
• Song, H., Witt, S. F., & Li, G. (2009). The advanced econometrics of tourism demand. Routledge.
   Google Scholar
• Sun, S., Wei, Y., Tsui, K.-L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1–10. https://doi.org/10.1016/j.tourman.2018.07.010
   Web of Science ®Google Scholar
• Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28. https://doi.org/10.1257/jep.28.2.3
   Web of Science ®Google Scholar
• Wickramasinghe, K., & Ratnasiri, S. (2021). The role of disaggregated search data in improving tourism forecasts: Evidence from Sri Lanka. Current Issues in Tourism, 24(19), 2740–2754. https://doi.org/10.1080/13683500.2020.1849049
   Web of Science ®Google Scholar
• Witt, S. F., & Witt, C. A. (1995). Forecasting tourism demand: A review of empirical research. International Journal of Forecasting, 11(3), 447–475. https://doi.org/10.1016/0169-2070(95)00591-7
   Web of Science ®Google Scholar
• Yang, X., Pan, B., Evans, J. A., & Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism Management, 46, 386–397. https://doi.org/10.1016/j.tourman.2014.07.019
   Web of Science ®Google Scholar
• Yao, L., Ma, R., & Wang, H. (2021). Baidu index-based forecast of daily tourist arrivals through rescaled range analysis, support vector regression, and autoregressive integrated moving average. Alexandria Engineering Journal, 60(1), 365–372. https://doi.org/10.1016/j.aej.2020.08.037
   Web of Science ®Google Scholar
• Zhang, B., Huang, X., Li, N., & Law, R. (2017). A novel hybrid model for tourist volume forecasting incorporating search engine data. Asia Pacific Journal of Tourism Research, 22(3), 245–254. https://doi.org/10.1080/10941665.2016.1232742
   Web of Science ®Google Scholar
• Zhang, B., Li, N., Shi, F., & Law, R. (2020). A deep learning approach for daily tourist flow forecasting with consumer search data. Asia Pacific Journal of Tourism Research, 25(3), 323–339. https://doi.org/10.1080/10941665.2019.1709876
   Web of Science ®Google Scholar
• Zhang, X., Liang, X., Zhiyuli, A., Zhang, S., Xu, R., & Wu, B. (2019). AT-LSTM: An attention-based LSTM model for financial time series prediction. IOP Conference Series: Materials Science and Engineering, 569(5), 052037. https://doi.org/10.1088/1757-899X/569/5/052037
   Google Scholar
• Zhang, Y., Li, G., Muskat, B., & Law, R. (2021). Tourism demand forecasting: A decomposed deep learning approach. Journal of Travel Research, 60(5), 981–997. https://doi.org/10.1177/0047287520919522
   Web of Science ®Google Scholar
• Zhao, E., Du, P., Azaglo, E. Y., Wang, S., & Sun, S. (2022). Forecasting daily tourism volume: A hybrid approach with CEMMDAN and multi-kernel adaptive ensemble. Current Issues in Tourism, 1–20. https://doi.org/10.1080/13683500.2022.2048806
   Web of Science ®Google Scholar
• Zheng, W., Huang, L., & Lin, Z. (2021). Multi-attraction, hourly tourism demand forecasting. Annals of Tourism Research, 90, 103271. https://doi.org/10.1016/j.annals.2021.103271
   Web of Science ®Google Scholar