Tourism demand forecasting using novel hybrid system

Pai, Ping-Feng; Hung, Kuo-Chen; Lin, Kuo-Ping

doi:10.1016/j.eswa.2013.12.007

Cited by 92 publications

(57 citation statements)

References 44 publications

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“…The availability of more advanced forecasting techniques has led to a growing interest Artificial Intelligence (AI) models (Yu, Schwartz 2006;Goh et al 2008;Lin et al 2011;Chen 2011;Celotto et al 2012;Wu et al 2012;Cang, Yu 2014) to the detriment of time series models (Chu 2008(Chu , 2011Assaf et al 2011) and causal econometric models (Page et al 2012). Some of the new AI based techniques are fuzzy time series models (Tsaur, Kuo 2011), genetic algorithms (Hadavandi et al 2011), expert systems (Shahrabi et al 2013;Pai et al 2014) and Support Vector Machines (SVMs) (Chen, Wang 2007;Hong et al 2011). Recent research has shown the suitability of Artificial Neural Networks (ANNs) for dealing with tourism demand forecasting (Teixeira, Fernandes 2012;Claveria, Torra 2014).…”

Section: Introductionmentioning

confidence: 99%

Data Pre-Processing for Neural Network-Based Forecasting: Does It Really Matter?

Clavería

Monte

Porras

2015

Technological and Economic Development of Economy

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Abstract.This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and Elman neural networks. The structure of the networks is based on a multiple-output approach. We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.Keywords: artificial neural networks, forecasting, multiple-output, seasonality, detrending, tourism demand, multilayer perceptron, radial basis function, Elman JEL classification: L83, C53, C45, R11 IntroductionInternational tourism has become one of today's fastest growing industries. Tourism accounts for almost 10% of total international trade and plays a fundamental role in the long-run economic development of many regions (Akkemik 2012;Sigala, Chalkiti 2014). To achieve a sustainable tourism model, policy makers and professionals need more accurate predictions of the number of tourist arrivals at the destination level. Many authors have acknowledged the importance of applying new approaches to tourism demand forecasting in order to improve the accuracy of the methods of analysis (Song, Li 2008). The availability of more advanced forecasting techniques has led to a growing interest Artificial Intelligence (AI) models (Yu, Schwartz 2006;Goh et al. 2008;Lin et al. 2011;Chen 2011;Celotto et al. 2012;Wu et al. 2012;Cang, Yu 2014) to the detriment of time series models (Chu 2008(Chu , 2011Assaf et al. 2011) and causal econometric models (Page et al. 2012). Some of the new AI based techniques are fuzzy time series models (Tsaur, Kuo 2011), genetic algorithms (Hadavandi et al. 2011), expert systems (Shahrabi et al. 2013;Pai et al. 2014) and Support Vector Machines (SVMs) (Chen, Wang 2007;Hong et al. 2011). Recent research has shown the suitability of Artificial Neural Networks (ANNs) for dealing with tourism demand forecasting (Teixeira, Fernandes 2012;Claveria, Torra 2014).In spite of the successful use of ANNs for time series forecasting, very few studies compare the accuracy of different NN architectures for tourism demand forecasting at a regional level. The present study deals with tourist a...

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Section: Introductionmentioning

confidence: 99%

Data Pre-Processing for Neural Network-Based Forecasting: Does It Really Matter?

Clavería

Monte

Porras

2015

Technological and Economic Development of Economy

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“…See Song, Dwyer, Li and Cao (2012) and Peng, Song, and Crouch (2014) for a thorough review of tourism economics research and tourism demand forecasting studies. Nevertheless, the need for more accurate forecasts has led to an increasing use of AI techniques, such as fuzzy time series models and support vector machines (SVMs), or a mix of them (Pai, Hung, & Lin 2014;Cang & Yu 2014), in order to obtain more refined predictions of tourist arrivals at the destination level. Yu and Schwartz (2006) and Huarng, Moutinho and Yuo (2007) use fuzzy time series models in predicting annual U.S. tourist arrivals and monthly tourism demand in Taiwan respectively.…”

Section: Tourism Demand Forecasting With Ai-based Techniquesmentioning

confidence: 99%

“…Song and Li (2008) have acknowledged the importance of applying new approaches to tourism demand forecasting in order to improve the accuracy and the performance of the methods of analysis. Whilst most research efforts focus on conventional tourism forecasting methods (Gounopoulos, Petmezas, & Santamaria, 2012) or a combination of them (Chan, Witt, Lee, & Song, 2010), in recent years the availability of more advanced forecasting techniques and the requirement for more accurate forecasts of tourism demand have led to a growing interest in Artificial Intelligence (AI) techniques (Wu, Law, & Xu, 2012;Cang, 2013;Pai, Hung, & Lin 2014). The suitability of AI models to handle nonlinear behaviour is one of the reasons why Artificial Neural Networks (ANNs) are increasingly used for forecasting purposes.…”

Section: Introductionmentioning

confidence: 99%

Common trends in international tourism demand: Are they useful to improve tourism predictions?

Clavería

Monte

Porras

2015

Tourism Management Perspectives

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ABSTRACT. This study evaluates whether modelling the existing common trends in tourist arrivals from all visitor markets to a specific destination can improve tourism predictions. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the multivariate multiple-output approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals.

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“…On the other hand, machine learning techniques are developed for time series forecasting, such as support vector machines [14]- [16], fuzzy time-series methods [17], rough set approaches [18], [19], genetic programming [20], artificial neural networks (ANNs) [21]- [28] and their hybridizations [29]- [32]. These complex non-linear models overcome the limitation of linear models as they are able to capture non-linear pattern of data, thus improving their prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Chen

Sun

Gao

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYWith the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

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Tourism demand forecasting using novel hybrid system

Cited by 92 publications

References 44 publications

Data Pre-Processing for Neural Network-Based Forecasting: Does It Really Matter?

Data Pre-Processing for Neural Network-Based Forecasting: Does It Really Matter?

Common trends in international tourism demand: Are they useful to improve tourism predictions?

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

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