The adaptive fuzzy time series model with an application to Taiwan’s tourism demand

Tsaur, Ruey-Chyn; Kuo, Tony C. T.

doi:10.1016/j.eswa.2011.01.059

Cited by 69 publications

(41 citation statements)

References 22 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A hybrid method based on the fuzzy system and genetic algorithms has been used by several studies (e.g. Hadavandi et al, 2011;Shahrabi et al, 2013;Tsaur and Kuo, 2011). Genetic algorithms have also been applied to a SVR model (e.g.…”

Section: Artificial Intelligence-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

New developments in tourism and hotel demand modeling and forecasting

Song

Shen

2017

IJCHM

243

217

View full text Add to dashboard Cite

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.

show abstract

Section: Artificial Intelligence-based Methodsmentioning

confidence: 99%

“…Cang, 2014;Chen and Wang, 2007;Hong et al, 2011;Xu et al, 2009). The fuzzy system model is suitable in circumstances where data are linguistic terms or comprise less than 50 data points (Tsaur and Kuo, 2011). Different versions of the fuzzy system model are used for tourism and hotel demand forecasting.…”

Section: Artificial Intelligence-based Methodsmentioning

confidence: 99%

New developments in tourism and hotel demand modeling and forecasting

Song

Shen

2017

IJCHM

243

217

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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...

show abstract

“…But the observations contained a large amount of incomplete or noise information, Fuzzy Time Series [1] introduces the fuzzy theory, and to express the uncertainty in the historical data using fuzzy variables, and the corresponding prediction method has been used in many fields successfully, such as student registration [1][2][3], temperature [4,5], travel [6] and so on, especially in Stock prediction [7].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Time Series Prediction Method Based on Spectral Clustering

Zhou¹,

Huang²,

Li³

et al. 2017

dtetr

View full text Add to dashboard Cite

Abstract. The accuracy of fuzzy time series prediction method depends on the accuracy of the discourse division and fuzzy relation extraction. Aiming at the main problems in the model, this paper put forward a kind of fuzzy time series prediction method based on spectral clustering using spectral clustering dividing time series discourse and calculate the corresponding fuzzy sets to construct fuzzy time series; At the same time using second-order Markov probability model constructing fuzzy relation in fuzzy time series, and then predict the subsequent numerical. Compared with the traditional fuzzy time series prediction method, this method can improve the accuracy.

show abstract

The adaptive fuzzy time series model with an application to Taiwan’s tourism demand

Cited by 69 publications

References 22 publications

New developments in tourism and hotel demand modeling and forecasting

New developments in tourism and hotel demand modeling and forecasting

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

A Fuzzy Time Series Prediction Method Based on Spectral Clustering

Contact Info

Product

Resources

About