Multivariate Exponential Smoothing for Forecasting Tourist Arrivals

Athanasopoulos, George; Silva, Ashton de

doi:10.1177/0047287511434115

Cited by 50 publications

(42 citation statements)

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“…For example, Athanasopoulos and Silva (2012) developed a new set of forecasting models dealing with local level and trend, and damped trend with an additive multivariate seasonal components to forecast the demand for tourism. Another example is Shahrabi et al (2013) who proposed a modular genetic-fuzzy forecasting system by combining genetic fuzzy expert and data preprocessing systems.…”

Section: Theoretical Implicationsmentioning

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: Theoretical Implicationsmentioning

confidence: 99%

New developments in tourism and hotel demand modeling and forecasting

Song

Shen

2017

IJCHM

255

217

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“…Multivariate approaches to tourist demand forecasting are few and have yielded mixed results. Athanasopoulos and Silva (2012) compared the forecasting accuracy of exponential smoothing methods in a multivariate setting against univariate alternatives. They used international tourist arrivals to Australia and New Zealand and found that multivariate models improved on forecast accuracy over the univariate alternatives.…”

Section: Design Of the Experimentsmentioning

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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“…While Athanasopoulos and Silva (2012) find that exponential smoothing methods in a multivariate setting improve the forecasting accuracy of univariate alternatives, du Preez and Witt (2003) obtain evidence that multivariate time series models do not generate more accurate forecasts than univariate time series models. When comparing the performance of different ANN models we are evaluating the impact of alternative ways of processing data on forecast accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Most research efforts apply econometric models such as static almost-ideal demand system (AIDS) models (Han et al, 2006), error correction (EC) and co-integration (CI) models (Veloce, 2004;Ouerfelli, 2008, Lee, 2011, vector autoregressive (VAR) models (Song and Witt, 2006), time varying parameter (TVP) models (Song et al, 2011). Choice models (Talluri and van Ryzin, 2004) have also been increasingly used in revenue management.Time series models such as exponential smoothing (Athanasopoulos et al, 2012) and autoregressive integrated moving average (ARIMA) models (Claveria and Datzira, 2010;Assaf et al, 2011;Gounopoulos et al, 2012) have been widely used in the literature. While there is no consensus on the most appropriate approach to forecast tourism demand (Kim and Schwartz, 2013), there seems to be unanimity on the importance of applying new approaches to tourism demand forecasting (Song and Li, 2008), and on the fact that nonlinear methods outperform linear methods in modelling economic behaviour (Cang, 2013).…”

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confidence: 99%

A new forecasting approach for the hospitality industry

Clavería

Monte

Porras

2015

International Journal of Contemporary Hospitality Management

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Purpose – This study aims to apply a new forecasting approach to improve predictions in the hospitality industry. To do so, the authors developed a multivariate setting that allows the incorporation of the cross-correlations in the evolution of tourist arrivals from visitor markets to a specific destination in neural network models.\ud Design/methodology/approach – This multiple-input-multiple-output approach allows the generation of predictions for all visitor markets simultaneously. Official data of tourist arrivals to Catalonia (Spain) from 2001 to 2012 were used to generate forecasts for one, three and six months ahead with three different networks.\ud Findings – The study revealed that multivariate architectures that take into account the connections between different markets may improve the predictive performance of neural networks. Additionally, the authors developed a new forecasting accuracy measure and found that radial basis function networks outperform the rest of the models.\ud Research limitations/implications – This research contributes to the hospitality literature by developing an innovative framework to improve the forecasting performance of artificial intelligence\ud techniques and by providing a new forecasting accuracy measure.\ud Practical implications – The proposed forecasting approach may prove very useful for planning purposes, helping managers to anticipate the evolution of variables related to the daily activity of the\ud industry.\ud Originality/value – A multivariate neural network framework has been developed to improve forecasting accuracy, providing professionals with an innovative and practical forecasting approachPeer ReviewedPostprint (author's final draft

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Multivariate Exponential Smoothing for Forecasting Tourist Arrivals

Cited by 50 publications

References 36 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 new forecasting approach for the hospitality industry

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