Comparative Analysis of Techniques for Forecasting Tourists’ Arrival (original) (raw)

Tourism demand forecasting – a review on the variables and models

Journal of Physics: Conference Series

With the growth of the world's tourism industry, researchers took advantage to conduct numerous studies in forecasting of tourism demand. The objective of this paper is to review the studies on tourism demand starting from 2010 to 2018 which varies on the explanatory variables, such as tourist income, exchange rate, gross domestic product, and others. In addition, this study also reviewed the models used to forecast and analyse tourism demand which are time-series model, econometric causal model and artificial intelligence model. The result from this review shows it is difficult to conclude which models performed the best for tourism demand. However, in most of the studies, combined models outperformed single model. Furthermore, the authors mentioned about the roles of tourism practitioners in the industry, tourism seasonality and suggestions for further studies in the future.

A Hybrid Intelligent Model for Tourism Demand Forecasting

Acta turistica, 2017

Rast turističke potražnje diljem svijeta dovela je do porasta broja metoda za prognoziranje turističke potražnje. Nove su tehnike polučile pouzdane prognoze turističkih dolazaka s ciljem boljeg ekonomskog planiranja. Ovo istraživanje ima za cilj prognozirati i usporediti djelotvornost dvaju nelinearnih pristupa umjetne inteligencije u predviđanju broja turističkih dolazaka u Singapur. Mjesečni podaci o dolasku turista u Singapur korišteni su za prognoziranje mjesec, dva, četiri i šest mjeseci unaprijed pomoću nelinearnih autoregresivnih (NAR) neuronskih mreža i neuro-fuzzy (neizrazitih) sustava. Točnost predviđanja neuronskih mreža NAR uspoređivala se s onom neuro-fuzzy sustava pomoću različitih mjerenja učinkovitosti. Studija je pokazala da su neuro-fuzzy sustavi učinkovitiji od mreže NAR u svim razdobljima prognoze i kod svih zemalja. Predložena neuro-fuzzy metoda poboljšava učinkovitost prognoziranja tehnika temeljenih na umjetnoj inteligenciji. Ova studija predstavlja doprinos literaturi u području turizma i mogu je koristiti menadžeri za učinkovito planiranje i provođenje mjera u okviru turističke politike. KLJUČNE RIJEČI: turistička potražnja; predviđanje; nelinearna autoregresivna neuronska mreža; prilagodljivi neuro-fuzzy (neizrazit) sustav zaključivanja ABSTRACT: The ever increasing demand of the tourism sector worldwide has led to an increase in tourism demand forecasting methodologies. New techniques yield much reliable predictions of tourist arrivals for better economic planning. The study aims to forecast and compare the performance of two non-linear artificial intelligence approaches in predicting the number of tourist arrivals to Singapore. The Singapore inbound monthly tourism data were utilized to generate one, two, four and six month ahead forecasts with non-linear autoregressive (NAR) neural networks and neuro-fuzzy systems. The predictive accuracy of NAR neural networks and neuro-fuzzy systems were compared with various performance metrics. The study revealed that neuro-fuzzy systems outperformed NAR networks in all forecasting horizons and for all countries. The proposed neuro-fuzzy methodology helps in improving the forecasting performance of artificial intelligence based techniques. The study contributes to hospitality literature and could be utilized by managers to effectively plan and implement tourism related policy measures.

Forecasting international tourism demand using time varying parameter error correction model

2005

This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays. The forecasting results reveal that non-linear methods achieve slightly better predictions than those obtained by a traditional forecasting technique, the seasonal autoregressive integrated moving average (SARIMA) approach. This slight forecasting improvement was close to being statistically significant. Forecasters must judge whether the high cost of implementing these computational methods is worthwhile.

Modelling and Forecasting International Tourism Demand – Evaluation of Forecasting Performance

International Journal of Business Administration, 2015

The paper examines the forecasting accuracy of different forecasting techniques in modelling and forecasting international tourism demand in Croatia. As tourist arrivals is the most commonly used measure of international tourism demand, the realized number of German tourists arrivals in the period from first quarter of 2003 to the last quarter of 2013 is taken as a measure of tourism demand in Croatia. In this paper following forecasting techniques are compared: the seasonal naïve model, the Holt-Winters triple exponential smoothing, the seasonal autoregressive integrated moving average model (SARIMA) and the multiple regression model. After approaching the forecasting procedure, all models are compared considering the in sample and the out of sample mean absolute percentage error (MAPE). All compared models show good forecasting performances. Although the diagnostics for the selected models reveals that the four models do not significantly differ, it can be concluded that multiple regression model perform a highly accurate forecasting of German tourists arrivals in Croatia.

Modelling and forecasting international tourist arrivals to mainland China= ������������������������������������: ��� General-Specific ������������

2007

The paper examines the forecasting accuracy of different forecasting techniques in modelling and forecasting international tourism demand in Croatia. As tourist arrivals is the most commonly used measure of international tourism demand, the realized number of German tourists arrivals in the period from first quarter of 2003 to the last quarter of 2013 is taken as a measure of tourism demand in Croatia. In this paper following forecasting techniques are compared: the seasonal naïve model, the Holt-Winters triple exponential smoothing, the seasonal autoregressive integrated moving average model (SARIMA) and the multiple regression model. After approaching the forecasting procedure, all models are compared considering the in sample and the out of sample mean absolute percentage error (MAPE). All compared models show good forecasting performances. Although the diagnostics for the selected models reveals that the four models do not significantly differ, it can be concluded that multiple regression model perform a highly accurate forecasting of German tourists arrivals in Croatia.

Tourism Demand Forecasting and Management

2017

This paper underlines the trend of tourism demand which is a foundation on which all tourism related business decisions ultimately rest. The paper provides a medium-term estimation of foreign tourism demand at Agra for the period ending by 2016. The time-series analysis was used as one of the quantitative methods commonly applied in estimations. After determining the trend, the strategies are generated for managing the tourism. A case study of Agra is also presented to show the real-life applicability of the developed methodology.

A Comparative Analysis of Three Types of Tourism Demand Forecasting Models: Individual, Linear Combination and Non-linear Combination

This paper investigates the combination of individual forecasting models and their roles in improving forecasting accuracy and proposes two non-linear combination forecasting models using Radial Basis Function and Support Vector Regression neural networks. These two nonlinear combination models plus the standard Multi-layer Perceptron neural network-based non-linear combination model are examined and compared with the linear combination models. The UK inbound tourism quarterly arrival data is used and the empirical results demonstrate that the proposed non-linear combination models are robust and outperform the linear combination models that currently dominate in the tourism forecasting literature.

Forecasting Dynamic Tourism Demand Using Artificial Neural Networks

Journal of Electrical Engineering and Information Technologies, 2021

Planning tourism development means preparing the destination for coping with uncertainties as tourism is sensitive to many changes. This study tested two types of artificial neural networks in modeling international tourist arrivals recorded in Ohrid (North Macedonia) during 2010-2019. It argues that the MultiLayer Perceptron (MLP) network is more accurate than the Nonlinear AutoRegressive eXogenous (NARX) model when forecasting tourism demand. The research reveales that the bigger the number of neurons may not necessarily lead to further performance improvement of the model. The MLP network for its better performance in modelling series with unexpected challenges is highly recommended for forecasting dynamic tourism demand.