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A mathematical analysis for the forecast research on tourism carrying capacity to promote the effective and sustainable development of tourism
2019
Discrete and Continuous Dynamical Systems. Series S
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In this paper, a new forecast approach is proposed for government staff and scenic spot management staff on tourist carrying capacity, which promotes the effective, healthy and sustainable development ...
The contradiction between the development of tourism economy and the protection of ecological environment has become the focus of scientific experts and Chinese government, and accordingly it is of vital ...
Authors would appreciate the anonymous reviewers for their insightful and constructive comments and suggestions, which have been contribute to improve the manuscript and our future research. ...
doi:10.3934/dcdss.2019056
fatcat:67iubfedlbbjtjnjspykayc2wu
A New Decomposition Ensemble Approach for Tourism Demand Forecasting: Evidence from Major Source Countries
[article]
2020
arXiv
pre-print
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empirical mode decomposition. ...
Accordingly, the decomposition ensemble learning approach is proposed to analyze the impact of different market factors on market demand, and the potential advantages of the proposed method on forecasting ...
countries and exploring their multi-scale relationship with the tourist destination; and (2) introducing the noise-assisted multivariate empirical mode decomposition-based appraoches into tourism demand ...
arXiv:2002.09201v1
fatcat:yzdw2xwmffh3xdvuzbvo2hknce
The Tourism Demand of Nonlinear Combination Forecasting based on Time Series Method and WNN
2015
International Journal of u- and e- Service, Science and Technology
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WNN combines the advantages of wavelet analysis and BP neural network and improves the learning efficiency and forecasting accuracy. ...
At last, the IOWGA-EMD-ARMA-WNN model is used to forecast monthly inboard tourism demand of China and the results show that the proposed combination model has better performance on forecasting accuracy ...
The Wavelet Neural Network Model Wavelet neural network is a typical feed forward neural network based on the theory of wavelet analysis. ...
doi:10.14257/ijunesst.2015.8.3.29
fatcat:5lqhnxk4wvd75kfwtfg6w5ab5m
Attention-Based STL-BiLSTM Network to Forecast Tourist Arrival
2021
Processes
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Machine learning models, and in particular, deep neural networks, can perform better than traditional forecasting models which depend mainly on past observations (e.g., past data) to forecast future tourist ...
Tourism makes a significant contribution to the economy of almost every country, so accurate demand forecasting can help in better planning for the government and a range of stakeholders involved in the ...
Acknowledgments: The authors would like to thank Sondoss El Sawah, Acting Director, Centre for System Capability, UNSW-Canberra at ADFA for her critical evaluation, numerous discussions, and helpful comments ...
doi:10.3390/pr9101759
fatcat:ginygfjzzzhbpm4yctfoafitpm
A decomposition-ensemble approach for tourism forecasting
2020
Annals of Tourism Research
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To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic ...
analysis, and the Elman's neural network model. ...
Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 71771207, 71642006), and the National Center for Mathematics and Interdisciplinary Sciences, CAS. ...
doi:10.1016/j.annals.2020.102891
pmid:32501311
pmcid:PMC7147863
fatcat:4egnusgq4vagrj23goh4id5pkm
Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach
[article]
2020
arXiv
pre-print
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In this study, a new adaptive multiscale ensemble (AME) learning approach incorporating variational mode decomposition (VMD) and least square support vector regression (LSSVR) is developed for short-, ...
Taking two accuracy measures and the Diebold-Mariano test, the empirical results demonstrate that our proposed AME learning approach can achieve higher level and directional forecasting accuracy compared ...
Additionally, SVR with GA has been used in tourism demand forecasting compared with Back Propagation Neural Network(BPNN) and ARIMA (Chen and Wang 2007) , as well as the use of gray theory and fuzzy time ...
arXiv:2002.08021v2
fatcat:344weapfmrdqtdynnpn2qhyrpu
Forecasting of Foreign Tourists' Arrivals in Bangladesh: A Neural Network Approach
2019
Zenodo
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Empirical results showed that neural network model outperformed other forecasting models. ...
One output variable and seven input variables were used. The output of the neural network model represented the foreign tourists' arrivals in Bangladesh. ...
Yahya, Samsudin and Shabri (2017) forecasted Tourism Demand Using Hybrid Modified Empirical Mode Decomposition (EMD) and neural network. ...
doi:10.5281/zenodo.4581094
fatcat:teoc36cd4zbtphghhsskihruzu
A Hybrid Approach on Tourism Demand Forecasting
2018
Journal of Physics, Conference Series
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In this study, an equally-weighted hybrid method, which is a combination of Box-Jenkins and Artificial Neural Networks, was applied to forecast Malaysia's tourism demand. ...
Tourism demand forecasting gives valuable information to policy makers, decision makers and organizations related to tourism industry in order to make crucial decision and planning. ...
This method is flexible in solving both linear and non-linear models by combining the forecast from both methods with the aim of enhancing forecasting performance [5] . ...
doi:10.1088/1742-6596/995/1/012034
fatcat:me6phvmpczflxfwzbjfpisi66e
A review of demand forecasting models and methodological developments within tourism and passenger transportation industry
2019
Journal of Tourism Futures
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Purpose -The purpose of this paper is to review the current literature in the field of tourism demand forecasting. ...
Design/methodology/approach -Published papers in the high quality journals are studied and categorized based their used forecasting method. ...
A model based upon both empirical mode decomposition and back-propagation neural (BPN) network was proposed by Chen, Lai and Yeh (2012) to predict tourism demand by the number of arrivals. ...
doi:10.1108/jtf-10-2018-0061
fatcat:yrh57fdybbfnjn3byl23rwnn3e
Tourism Traffic Demand Prediction Using Google Trends Based on EEMD-DBN
2020
Engineering
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This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand ...
Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. ...
CCNU19ZN024 and the Humanities and Social Sciences Layout Foundation of the Ministry of Education of China under Grant No. 20YJA740047. ...
doi:10.4236/eng.2020.123016
fatcat:x6ayhwzsvvc4tnitbi75hhxsz4
A Hybrid of EEMD and LSSVM-PSO model for Tourist Demand Forecasting
2016
Indian Journal of Science and Technology
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In this research, hybrid model of Least Square Support Vector Machine (LSSVM) and Ensemble Empirical Mode Decomposition (EEMD) are presented to forecast tourism demand in Malaysia. ...
Foremost, the original series of tourism arrivals data was separated using EEMD technique into residual and Intrinsic Mode Functions (IMFs) components. ...
Acknowledgments The authors gratefully recognized the financial support from MOE, UTM and GUP Grant (VOT 4F681). ...
doi:10.17485/ijst/2016/v9i36/97773
fatcat:pwdtbvmkonhpla2zh7vbpleio4
Weekly Hotel Occupancy Forecasting of a Tourism Destination
2018
Sustainability
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The accurate forecasting of tourism demand is complicated by the dynamic tourism marketplace and its intricate causal relationships with economic factors. ...
In order to enhance forecasting accuracy, we present a modified ensemble empirical mode decomposition (EEMD)–autoregressive integrated moving average (ARIMA) model, which dissects a time series into three ...
Acknowledgments: Qing Zhu of the International Business School of Shaanxi Normal University offered tremendous guidance and help in research methodology. ...
doi:10.3390/su10124351
fatcat:ekbrnhsywnc65kkwtzs6ii665q
FORECASTING TOURISM DEMAND: A CASE STUDY FOCUSING ON SPANISH TOURIST' S TRAVEL TO CAPPADOCIA
2020
Pamukkale University Journal of Social Sciences Institute
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However, it is not always possible to know the actual demand and one can only make forecasts in such cases. ...
This paper deals with forecasting international tourism demand, specifically focusing on the Spanish tourist visits in Cappadocia region of Turkey. ...
DF: Monthly, RF: Turkey (D), FF: No, RT: Importance of Argo-touris Chen et al. (2012) In this study, tourism demand is predicted. Neural network and empirical mode decomposition (EMD) is used. ...
doi:10.30794/pausbed.679682
fatcat:klono4n2sfaqdmrjt6bg4fqltm
Tourism forecasting by search engine data with noise-processing
English
2016
African Journal of Business Management
English
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The study concluded that noise-processing was necessary for the tourism forecasting with search engine data, and HHT could be an effective method on denoising. ...
Moreover, wavelet transform and filtering were compared with HHT on denoising and the results implied that HHT had higher signal noise ratio (SNR) and forecast more accurately. ...
ACKNOWLEDGEMENTS This paper is supported by National Natural Science Foundation of China (Grant No.71172199 and 71202115). ...
doi:10.5897/ajbm2015.7945
fatcat:74sqjzhrevdqteecsprk5fxasm
Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting
2016
Sustainability
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To achieve higher forecasting accuracy, some signal processing algorithms, such as EMD (Empirical Mode Decomposition) [34], EEMD (Ensemble Empirical Mode Decomposition) algorithm [35] and FEEMD (Fast Ensemble ...
BP (back propagation) neural networks for wind speed forecasting. ...
This model combines the fast ensemble empirical mode decomposition technique and the ABBP mode, which uses the adaptive boosting (AB) strategy on BP neural networks and the hybrid optimization algorithm ...
doi:10.3390/su8030235
fatcat:jl6phthqbrbjhbkax4x75atfyu
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