Tourist Arrival Forecasting Using Multiscale Mode Learning Model
Abstract
:1. Introduction
2. Methodology
2.1. Mode Decomposition (MD) Model
- (1)
- Find all local maximum and minimum points of .
- (2)
- Use two separate cubic spline curves to connect all local maximum and minimum points for constructing the upper envelope and the lower envelope, respectively.
- (3)
- Calculate the local mean curve of the upper and lower envelopes.
- (4)
- Calculate the difference between the local mean curve and the data.
- (5)
- Repeat the previous steps until the difference satisfies the assumption of IMFs.
- (6)
- Calculate the residual. Use the residual to replace the original signal as a new . Repeat the previous steps until no more IMFs can be identified or the stopping criteria are satisfied.
- (7)
- Eventually, the original tourist arrival data after m repetitions can be expressed as the sum of IMF components and a residual component, as in Equation (1):
2.2. Convolutional Neural Network Model
2.3. MD-CNN Forecasting Model
3. Empirical Results
Data Description and Descriptive Statistics
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Dharmaratne, G.S. Forecasting tourist arrivals in Barbados. Ann. Tour. Res. 1995, 22, 804–818. [Google Scholar] [CrossRef]
- Hadavandi, E.; Ghanbari, A.; Shahanaghi, K.; Abbasian-Naghneh, S. Tourist arrival forecasting by evolutionary fuzzy systems. Tour. Manag. 2011, 32, 1196–1203. [Google Scholar] [CrossRef]
- Yang, Y.; Chen, M.H.; Su, C.H.J.; Lin, Y.X. Asymmetric effects of tourist arrivals on the hospitality industry. Int. J. Hosp. Manag. 2020, 84, 102323. [Google Scholar] [CrossRef]
- Deng, T.; Gan, C.; Du, H.; Hu, Y.; Wang, D. Do high speed rail configurations matter to tourist arrivals? Empirical evidence from China’s prefecture-level cities. Res. Transp. Econom. 2020, 90, 100952. [Google Scholar] [CrossRef]
- Nguyen, J.; Valadkhani, A. Dynamic responses of tourist arrivals in Australia to currency fluctuations. J. Hosp. Tour. Manag. 2020, 45, 71–78. [Google Scholar] [CrossRef]
- Huang, L.; Yin, X.; Yang, Y.; Luo, M.; Huang, S.S. Blessing in disguise: The impact of the Wenchuan earthquake on inbound tourist arrivals in Sichuan, China. J. Hosp. Tour. Manag. 2020, 42, 58–66. [Google Scholar] [CrossRef]
- Demir, E.; Simonyan, S.; Chen, M.H.; Marco Lau, C.K. Asymmetric effects of geopolitical risks on Turkey’s tourist arrivals. J. Hosp. Tour. Manag. 2020, 45, 23–26. [Google Scholar] [CrossRef]
- Tiwari, A.K.; Das, D.; Dutta, A. Geopolitical risk, economic policy uncertainty and tourist arrivals: Evidence from a developing country. Tour. Manag. 2019, 75, 323–327. [Google Scholar] [CrossRef]
- Fourie, J.; Santana-Gallego, M. The impact of mega-sport events on tourist arrivals. Tour. Manag. 2011, 32, 1364–1370. [Google Scholar] [CrossRef]
- Mao, C.K.; Ding, C.G.; Lee, H.Y. Post-SARS tourist arrival recovery patterns: An analysis based on a catastrophe theory. Tour. Manag. 2010, 31, 855–861. [Google Scholar] [CrossRef]
- Su, Y.W.; Lin, H.L. Analysis of international tourist arrivals worldwide: The role of world heritage sites. Tour. Manag. 2014, 40, 46–58. [Google Scholar] [CrossRef]
- Yang, C.H.; Lin, H.L.; Han, C.C. Analysis of international tourist arrivals in China: The role of World Heritage Sites. Tour. Manag. 2010, 31, 827–837. [Google Scholar] [CrossRef] [PubMed]
- Bi, J.-W.; Li, H.; Fan, Z.-P. Tourism demand forecasting with time series imaging: A deep learning model. Ann. Tour. Res. 2021, 90, 103255. [Google Scholar] [CrossRef]
- Song, H.; Wen, L.; Liu, C. Density tourism demand forecasting revisited. Ann. Tour. Res. 2019, 75, 379–392. [Google Scholar] [CrossRef]
- Gounopoulos, D.; Petmezas, D.; Santamaria, D. Forecasting Tourist Arrivals in Greece and the Impact of Macroeconomic Shocks from the Countries of Tourists? Origin. Ann. Tour. Res. 2012, 39, 641–666. [Google Scholar] [CrossRef]
- Song, H.; Li, G. Tourism demand modelling and forecasting—A review of recent research. Tour. Manag. 2008, 29, 203–220. [Google Scholar] [CrossRef]
- Song, H.; Qiu, R.T.; Park, J. A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Ann. Tour. Res. 2019, 75, 338–362. [Google Scholar] [CrossRef]
- Yang, X.; Pan, B.; Evans, J.A.; Lv, B. Forecasting Chinese tourist volume with search engine data. Tour. Manag. 2015, 46, 386–397. [Google Scholar] [CrossRef]
- Vergori, A.S. Patterns of seasonality and tourism demand forecasting. Tour. Econ. 2017, 23, 1011–1027. [Google Scholar] [CrossRef]
- Hassani, H.; Silva, E.S.; Antonakakis, N.; Filis, G.; Gupta, R. Forecasting accuracy evaluation of tourist arrivals. Ann. Tour. Res. 2017, 63, 112–127. [Google Scholar] [CrossRef]
- Li, S.; Chen, T.; Wang, L.; Ming, C. Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index. Tour. Manag. 2018, 68, 116–126. [Google Scholar] [CrossRef]
- Cang, S. A Comparative Analysis of Three Types of Tourism Demand Forecasting Models: Individual, Linear Combination and Non-linear Combination. Int. J. Tour. Res. 2014, 16, 596–607. [Google Scholar] [CrossRef]
- Long, W.; Lu, Z.; Cui, L. Deep learning-based feature engineering for stock price movement prediction. Knowl. Based Syst. 2019, 164, 163–173. [Google Scholar] [CrossRef]
- Lahmiri, S.; Bekiros, S. Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos Solitons Fractals 2019, 118, 35–40. [Google Scholar] [CrossRef]
- Law, R.; Li, G.; Fong, D.K.C.; Han, X. Tourism demand forecasting: A deep learning approach. Ann. Tour. Res. 2019, 75, 410–423. [Google Scholar] [CrossRef]
- Kulshrestha, A.; Krishnaswamy, V.; Sharma, M. Bayesian BILSTM approach for tourism demand forecasting. Ann. Tour. Res. 2020, 83, 102925. [Google Scholar] [CrossRef]
- Bi, J.W.; Liu, Y.; Li, H. Daily tourism volume forecasting for tourist attractions. Ann. Tour. Res. 2020, 83, 102923. [Google Scholar] [CrossRef]
- Chen, J.; Zhu, X.; Zhong, M. Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: A Markov-switching VAR analysis. Resour. Policy 2018, 61, 489–500. [Google Scholar] [CrossRef]
- Tzirakis, P.; Trigeorgis, G.; Nicolaou, M.A.; Schuller, B.W.; Zafeiriou, S. End-to-End Multimodal Emotion Recognition Using Deep Neural Networks. IEEE J. Sel. Top. Signal Process. 2017, 11, 1301–1309. [Google Scholar] [CrossRef]
- McCann, M.T.; Jin, K.H.; Unser, M. Convolutional Neural Networks for Inverse Problems in Imaging A review. IEEE Signal Process. Mag. 2017, 34, 85–95. [Google Scholar] [CrossRef]
- Rawat, W.; Wang, Z. Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review. Neural Comput. 2017, 29, 2352–2449. [Google Scholar] [CrossRef]
- Lago, J.; Ridder, F.D.; Schutter, B.D. Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms. Appl. Energy 2018, 221, 386–405, Erratum in Appl. Energy 2018, 229, 1286. [Google Scholar] [CrossRef]
- Peng, B.; Song, H.; Crouch, G.I. A meta-analysis of international tourism demand forecasting and implications for practice. Tour. Manag. 2014, 45, 181–193. [Google Scholar] [CrossRef]
- Coshall, J.T.; Charlesworth, R. A management orientated approach to combination forecasting of tourism demand. Tour. Manag. 2011, 32, 759–769. [Google Scholar] [CrossRef]
- Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yen, N.C.; Tung, C.C.; Liu, H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 1998, 454, 903–995. [Google Scholar] [CrossRef]
- Zhang, C.; Jiang, F.; Wang, S.; Sun, S. A new decomposition ensemble approach for tourism demand forecasting: Evidence from major source countries in Asia-Pacific region. Int. J. Tour. Res. 2021, 23, 832–845. [Google Scholar] [CrossRef]
- Xing, G.; Sun, S.; Bi, D.; Guo, J.E.; Wang, S. Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach. Int. J. Tour. Res. 2022, 24, 425–442. [Google Scholar] [CrossRef]
- Xie, G.; Qian, Y.; Wang, S. A decomposition-ensemble approach for tourism forecasting. Ann. Tour. Res. 2020, 81, 102891. [Google Scholar] [CrossRef]
- Wu, Z.; Huang, N.E. Empirical mode decomposition: A noise-assisted data analysis method. Adv. Adapt. Data Anal. 2009, 1, 1–41. [Google Scholar] [CrossRef]
- Dragomiretskiy, K.; Zosso, D. Variational Mode Decomposition. IEEE Trans. Signal Process. 2014, 62, 531–544. [Google Scholar] [CrossRef]
- Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 1998, 86, 2278–2324. [Google Scholar] [CrossRef]
- van Noord, N.; Postma, E. Learning scale-variant and scale-invariant features for deep image classification. Pattern Recognit. 2017, 61, 583–592. [Google Scholar] [CrossRef]
- Zou, Y.; Yu, L.; Tso, G.K.; He, K. Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach. Phys. A: Stat. Mech. Appl. 2020, 541, 123360. [Google Scholar] [CrossRef]
- He, K.; Tso, G.K.; Zou, Y.; Liu, J. Crude oil risk forecasting: New evidence from multiscale analysis approach. Energy Econ. 2018, 76, 574–583. [Google Scholar] [CrossRef]
- He, K.; Zou, Y. Crude oil risk forecasting using mode decomposition based model. Procedia Comput. Sci. 2022, 199, 309–314. [Google Scholar] [CrossRef]
- Wang, J.; Wang, J. Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Netw. 2017, 90, 8–20. [Google Scholar] [CrossRef]
- Qiu, X.; Ren, Y.; Suganthan, P.N.; Amaratunga, G.A. Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting. Appl. Soft Comput. 2017, 54, 246–255. [Google Scholar] [CrossRef]
- Torres, M.E.; Colominas, M.A.; Schlotthauer, G.; Flandrin, P. A complete ensemble empirical mode decomposition with adaptive noise. In Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 22–27 May 2011; pp. 4144–4147. [Google Scholar] [CrossRef]
- Yeh, J.R.; Shieh, J.S.; Huang, N.E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Adv. Adapt. Data Anal. 2010, 2, 135–156. [Google Scholar] [CrossRef]
- Liu, T.; Luo, Z.; Huang, J.; Yan, S. A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications. Sensors 2018, 18, 2120. [Google Scholar] [CrossRef]
- Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw. 2015, 61, 85–117. [Google Scholar] [CrossRef]
- Hinton, G.E.; Salakhutdinov, R.R. Reducing the dimensionality of data with neural networks. Science 2006, 313, 504. [Google Scholar] [CrossRef] [PubMed]
- Hinton, G.E.; Osindero, S.; Teh, Y.W. A Fast Learning Algorithm for Deep Belief Nets. Neural Comput. 2014, 18, 1527–1554. [Google Scholar] [CrossRef] [PubMed]
- Liu, C.; Hou, W.; Liu, D. Foreign Exchange Rates Forecasting with Convolutional Neural Network. Neural Process. Lett. 2017, 46, 1095–1119. [Google Scholar] [CrossRef]
- Zhao, Z.; Zhang, Y.; Deng, Y.; Zhang, X. ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation. Comput. Biol. Med. 2018, 102, 168–179. [Google Scholar] [CrossRef] [PubMed]
- Lee, W.Y.; Park, S.M.; Sim, K.B. Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm. Optik 2018, 172, 359–367. [Google Scholar] [CrossRef]
Statistics | Skewness | Kurtosis | ||||
---|---|---|---|---|---|---|
China | 6.6678 | 1.7571 | 1.4807 | 7.5316 | 0.001 | 0 |
HK | 1.7382 | 4.7507 | 1.6218 | 7.7804 | 0.001 | 0 |
Indonesia | 0.0508 | 0.0290 | 2.1270 | 9.0066 | 0.001 | 0 |
Philippines | 0.0862 | 0.0333 | 1.7717 | 8.8315 | 0.001 | 0 |
Singapore | 0.0374 | 0.0150 | 0.6695 | 3.2716 | 0.001 | 0 |
Models | MSE | MSE | MSE | MSE | MSE |
---|---|---|---|---|---|
1.6773 | 1.1280 | 2.3589 | 7.8064 | 9.6990 | |
1.7660 | 1.1260 | 2.7853 | 10.5543 | 8.3517 | |
1.7654 | 1.1214 | 2.2340 | 8.2777 | 8.6854 | |
1.7365 | 1.2332 | 2.3452 | 9.2343 | 8.4691 |
Models | MSE | MSE | MSE | MSE | MSE |
---|---|---|---|---|---|
RW | 5.7607 | 4.4349 | 8.3045 | 3.1473 | 21.5561 |
ARIMA | 2.0019 | 2.0368 | 5.4306 | 1.7129 | 9.8781 |
Seasonal ARIMA | 1.8434 | 2.2151 | 5.8317 | 1.9324 | 5.5029 |
MD-CNN | 1.5185 | 1.6382 | 2.8709 | 1.2432 | 5.0741 |
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He, K.; Wu, D.; Zou, Y. Tourist Arrival Forecasting Using Multiscale Mode Learning Model. Mathematics 2022, 10, 2999. https://doi.org/10.3390/math10162999
He K, Wu D, Zou Y. Tourist Arrival Forecasting Using Multiscale Mode Learning Model. Mathematics. 2022; 10(16):2999. https://doi.org/10.3390/math10162999
Chicago/Turabian StyleHe, Kaijian, Don Wu, and Yingchao Zou. 2022. "Tourist Arrival Forecasting Using Multiscale Mode Learning Model" Mathematics 10, no. 16: 2999. https://doi.org/10.3390/math10162999
APA StyleHe, K., Wu, D., & Zou, Y. (2022). Tourist Arrival Forecasting Using Multiscale Mode Learning Model. Mathematics, 10(16), 2999. https://doi.org/10.3390/math10162999