Forecasting tourism demand based on empirical mode decomposition and neural network

Chen, Chun-Fu; Lai, Ming-Cheng; Yeh, Ching-Chiang

doi:10.1016/j.knosys.2011.09.002

Cited by 185 publications

(108 citation statements)

References 31 publications

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“…MLP is the most widely used ANN model; it contains three or more layers of neurons with nonlinear activation function (e.g. Chen et al, 2012;Claveria and Torra, 2014;Lin et al, 2011). As an alternative, Claveria et al (2015a), and Cuhadar et al (2014).…”

Section: Artificial Intelligence-based Methodsmentioning

confidence: 99%

New developments in tourism and hotel demand modeling and forecasting

Song

Shen

2017

IJCHM

255

217

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Design/Methodology/approachArticles on tourism and hotel demand modeling and forecasting published in both Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) journals were identified and analyzed. FindingsThis review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior, and consumer confidence indicators, among others. More sophisticated techniques such as Research limitations/implicationsThe main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implicationsThis review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/valueThe value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

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Section: Artificial Intelligence-based Methodsmentioning

confidence: 99%

New developments in tourism and hotel demand modeling and forecasting

Song

Shen

2017

IJCHM

255

217

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“…Forecasting results were computed exclusively for the following testing sample. We note that in the literature, EMD is instead sometimes applied to the whole dataset, including the testing part (see, e.g., Chen et al 2012;Cheng and Wei 2014;Lin et al 2012;Lu and Shao 2012;Wang et al 2014;Yu et al 2008). The inclusion of (future) testing data in the forecasting methodology is clearly wrong, providing meaningless "forecasts".…”

Section: Datamentioning

confidence: 99%

Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression

Nava

Matteo

Aste

2018

Risks

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Abstract:We introduce a multistep-ahead forecasting methodology that combines empirical mode decomposition (EMD) and support vector regression (SVR). This methodology is based on the idea that the forecasting task is simplified by using as input for SVR the time series decomposed with EMD. The outcomes of this methodology are compared with benchmark models commonly used in the literature. The results demonstrate that the combination of EMD and SVR can outperform benchmark models significantly, predicting the Standard & Poor's 500 Index from 30 s to 25 min ahead. The high-frequency components better forecast short-term horizons, whereas the low-frequency components better forecast long-term horizons.

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“…Zhang et al (2011) pointed out relationship bonding as the bonds between customers and brand attitudes to enhance customer loyalty. Chen et al (2012) considered that relationship bonding was developed with the accumulation of investment and common techniques. When relationship bonding was completed, both parties would realize the irreversibility of the invested relationship resources that they would not easily stop the relationship.…”

Section: Customer Loyaltymentioning

confidence: 99%

Effects of Relationship Bonding on Brand Attitudes and Customer Loyalty in Tourism Industry

Tai

2015

Acta Oeconomica

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The fi ercely competitive markets in tourism industry have caused diffi culty in developing new consumers. In addition to keep the quality of service, a tourism business has to change the marketing strategies and focus on the maintenance of relationship bonding with current consumers. In this study, the employees and customers of Chateau Beach Resort Kenting are distributed 300 copies of questionnaire. After deducting invalid and incomplete ones, total 162 valid copies are retrieved, with the retrieval rate 54%. The research results are summarized as follows. 1. Relationship bonding presents signifi cantly positive correlations with brand attitudes. 2. Brand attitudes reveal remarkably positive correlations with customer loyalty. 3. Relationship bonding shows notably positive correlations with customer loyalty. 4. Brand attitudes have partial mediating effects on the correlations between relationship bonding and customer loyalty. Aiming at above results, suggestions are proposed to help tourism businesses create better operation performance.

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Forecasting tourism demand based on empirical mode decomposition and neural network

Cited by 185 publications

References 31 publications

New developments in tourism and hotel demand modeling and forecasting

New developments in tourism and hotel demand modeling and forecasting

Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression

Effects of Relationship Bonding on Brand Attitudes and Customer Loyalty in Tourism Industry

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