Forecasting tourist arrivals to Balearic Islands using genetic programming

Álvarez‐Díaz, Marcos; Mateu-Sbert, Josep; Nadal, Jaume Rosselló

doi:10.1504/ijcee.2009.029153

Cited by 19 publications

(3 citation statements)

References 33 publications

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“…As part of Alvarez [3]'s research, genetic programming (GP) is incorporated into the study to predict the arrival of tourists from the United Kingdom and Germany on the Balearic Islands. This study found that the proposed model was highly accurate and stable compared to the NC model, the MA model, and the ARIMA model.…”

Section: Tourism Demand Forecasting Methods and Approachesmentioning

confidence: 99%

A Review of the Methods and Techniques Used in Tourism Demand Forecasting

Jassim¹,

Jetly²,

Abushakra

et al. 2023

EAI Endorsed Trans Creat Tech

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The purpose of this paper is to discuss the methodology and results of researchers who conducted a study concerning the forecasting of tourism demand. In more detail, this study aims to examine and assess various studies about search engines, web traffic data, and social media data, specifically. Using an extensive database of indexed articles, we conducted the review with the goal of providing a solid understanding of the literature. The findings of our study revealed that few researchers integrate different data sources when forecasting tourism demand. Therefore, the authors of this paper decided to conduct a systematic review to provide researchers with a comprehensive overview of the importance of such data. This paper may inspire Omani researchers to undertake similar research based on its findings, which is currently lacking. Thus, this paper will improve understanding of how data sources affect forecasting accuracy and how modern technologies can support economic growth.

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Section: Tourism Demand Forecasting Methods and Approachesmentioning

confidence: 99%

A Review of the Methods and Techniques Used in Tourism Demand Forecasting

Jassim¹,

Jetly²,

Abushakra

et al. 2023

EAI Endorsed Trans Creat Tech

View full text Add to dashboard Cite

show abstract

“…Despite the importance of Andalusia as a tourist destination, as far as we know, there is no research studies dealing with its inbound international tourism demand. Previous studies have modelled the tourism demand either for Spain as a whole (Garín-Muñoz & Pérez-Amaral, 2000) or for some other Spanish regions: Balearic Islands (Á lvarez Díaz et al, 2009;Garín-Muñoz & Montero-Martín, 2007;Rodríguez, 2017), Canary Islands (Garín-Muñoz, 2006;Gil-Alana, 2010;Ledesma-Rodríguez et al, 2001), Catalonia (Claveria & Torra, 2014;Turrion-Prats & Duro, 2017), Galicia (Garín-Muñoz, 2009;Otero-Giraldez et al, 2012) and the Mediterranean coast (Albaladejo & González-Martínez, 2018).…”

Section: Introductionmentioning

confidence: 99%

Foreign Tourism in Andalusia: A Dynamic Panel Data Analysis

2020

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This paper studies the main determinants of the inbound international tourism in Andalusia and quantify its incidence. Based on the classical theoretical framework for tourism demand, we incorporate dynamics into the model by adding the lagged dependent variable as an explanatory variable, along with the per capita income of the tourist's country of origin, the relative prices between the origin and destination countries and the cost of travel. The empirical model is applied to a panel data set consisting of 21 countries of origin of the tourists for the period 2008-2018. Data were collected from the Hotel Occupancy Survey (HOS), published by the National Statistics Institute of Spain (INE).The results have been obtained using the GMM-DIFF estimator of Arellano and Bond.The parameters estimated reflect a high level of consumer loyalty and the importance of the word-of-mouth effect. Moreover, the income elasticity indicates that the demand for tourism in Andalusia may be considered as a luxury good. Prices have a negative relationship with tourism demand. The cost of travel, which has a negative effect, is statistically significant to explain the number of tourists' arrivals and, however, it is not significant for the overnight stays model.

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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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Forecasting tourist arrivals to Balearic Islands using genetic programming

Cited by 19 publications

References 33 publications

A Review of the Methods and Techniques Used in Tourism Demand Forecasting

A Review of the Methods and Techniques Used in Tourism Demand Forecasting

Foreign Tourism in Andalusia: A Dynamic Panel Data Analysis

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

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