Short-medium Term Tourist Services Demand Forecasting with Rough Set Theory

Celotto, Emilio; Ellero, Andrea; Ferretti, Paola

doi:10.1016/s2212-5671(12)00121-9

Cited by 11 publications

(6 citation statements)

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“…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).…”

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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“…The image of the destination and the perception of the image of the visitors is constituted in the brand value of a destination, and becomes an axis of development, in the economic and marketing part, the latter generates a value in the minds of the Tourists, translated in the interest for the tourist demand that have led to conduct several studies that have led to the development of behavioral models [8] [9] [10], of the tourist and the selection of the place that visits, landing in factors of study as: needs, motivation, perception, attitude, personality, image. Social factors: lifestyle, family life cycle, family, social class; situational factors: opinions, physical and social environment, time, mood.…”

Section: Introductionmentioning

confidence: 99%

Tourism Marketing: Measuring Tourist Satisfaction

Analuiza¹,

Quisimalín²,

Pablos³

et al. 2017

JSSM

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The research was aimed at identifying and validating determinants of tourist satisfaction. The study area was the provinces of Chimborazo, Cotopaxi, Pastaza, Tungurahua, defined in Zone 3 of Ecuador, which transcended their geostrategic commercial position in the center of the country. In this context, the main objective of the study was to measure tourist satisfaction and to evaluate its determinants defined in variables such as product, price, distribution and tourist service as secondary axes of scope and transversal design. The sample synthesized an unknown sampling frame of 610 random tourists, representative sample where a semi-structured personal survey of 34 questions was applied considering 46 moderate variables and 9 classification variables. The statistical techniques used correspond to the partial least squares (PLS) method to give consistency to four items of product, two of the price, three of the distribution, one of the promotion and finally five of the service that allowed. All this was validated with the internal consistency of the model through composite relativity (CR), and Cron Bach's alpha, convergent validity was analyzed using the mean variance extracted (AVE), the structural model was examined through the coefficient Of determination (R2) and the Path (β) values, determined that this relationship is positive and consistent between variables of infrastructure, attention, cleanliness of the establishment and availability of parking; food and fun; ease of finding places and availability of service information; gastronomic and cultural tourism, positive tourism experience, successful choice of destination, fulfilled expectations, repetition of the trip and recommendation of destination.

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“…Silva and Hassani (2015) evaluate the impact of the 2008 recession on US trade by means of Singular Spectrum Analysis (SSA). The availability of more advanced forecasting techniques has led to a growing interest Artificial Intelligence (AI) methods (Gharleghi et al, 2014;Cang, 2014;Pai et al, 2014;Celotto et al, 2012;Chen, 2011;Lin et al, 2011;Goh et al, 2008;Yu and Schwartz, 2006) to the detriment of time series models (Chu, 2008(Chu, , 2011 and causal econometric models (Franses et al, 2011). Some of the most commonly used AI-based techniques in economics and finance are fuzzy time series models (Tsaur and Kuo, 2011), Support Vector Machines (SVMs) (Kao et al, 2013) and ANNs.…”

Section: Introductionmentioning

confidence: 99%

A self-organizing map analysis of survey-based agents׳ expectations before impending shocks for model selection: The case of the 2008 financial crisis

Clavería

Monte

Porras

2016

International Economics

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A self-organizing map analysis of survey-based agents' expectations before impending shocks for model selection: The case of the 2008 financial crisis ABSTRACT By means of Self-Organizing Maps we cluster fourteen European countries according to the most suitable way to model their agents' expectations. Using the financial crisis of 2008 as a benchmark, we distinguish between those countries that show a progressive anticipation of the crisis and those where sudden changes in expectations occur. We compare the forecasting performance of Artificial Neural Networks to that of three different time series models and find that neural networks outperform time series models when there are brisk changes in expectations before impending shocks. Conversely, in countries where expectations show a smooth transition towards recession, ARIMA models outperform neural networks. This result demonstrates the usefulness of clustering techniques for selecting the most appropriate method to forecast expectations according to their behaviour.

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Short-medium Term Tourist Services Demand Forecasting with Rough Set Theory

Cited by 11 publications

References 10 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?

Tourism Marketing: Measuring Tourist Satisfaction

A self-organizing map analysis of survey-based agents׳ expectations before impending shocks for model selection: The case of the 2008 financial crisis

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