Tourism demand forecasting – a review on the variables and models

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

Forecasting tomorrow’s tourist

Purpose: This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016. Design/methodology/approach: For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe. Findings: The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques. Originality/value: The present literature review offers recent insights on tourism forecasting scientific literature, providing evidences on current trends and revealing interesting research gaps.info:eu-repo/semantics/submittedVersio

Prediksi Kedatangan Turis Asing Ke Indonesia Menggunakan Backpropagation Neural Networks

In this paper, a backpropagation neural network (BPNN) method with time series data have been explored. The BPNN method to predict the foreign tourist's arrival to Indonesia datasets have been implemented. The foreign tourist's arrival datasets were taken from the center agency on statistics (BPS) Indonesia. The experimental results showed that the BPNN method with two hidden layers were able to forecast foreign tourist's arrival to Indonesia. Where, the mean square error (MSE) as forecasting accuracy has been indicated. In this study, the BPNN method is able and recommended to be alternative methods for predicting time series datasets. Also, the BPNN method showed that effective and easy to use. In other words, BPNN method is capable to producing good value of forecasting.Keywords - BPNN; foreign tourists; BPS; MSEPemanfaatan backpropagation neural network (BPNN) dengan data deret waktu telah digunakan dalam paper ini. Metode BPNN telah digunakan untuk memprediksi data kedatangan turis asing ke Indonesia, dimana data turis tersebut diambil dari badan pusat statistik Indonesia (BPS). Hasil pengujian menunjukkan bahwa metode BPNN dengan dua lapisan tersembunyi mampu memodelkan dan meramalkan data kedatangan turis asing ke Indonesia yang diindikasikan dengan nilai mean square error (MSE). Penelitian ini merekomendasikan bahwa metode BPNN mampu menjadi alternative metode dalam memprediksi data yang berjenis deret waktu karena metode BPNN efektif dan lebih mudah digunakan serta mampu menghasilkan akurasi nilai peramalan yang baik

Oleotourism as a sustainable product: an analysis of its demand in the South of Spain (Andalusia)

Olive oil has generated a new tourism offer in Spain called oleotourism. Visitors can enjoy landscapes of ancient olive groves and visit its oil mills called almazaras, to learn about its manufacture and to taste different oil varieties. Andalusia, located in the south of Spain, produces 60% of Spain’s olive oil, having the largest number of almazaras, and therefore most oleotourism offers. This differentiated tourism offer requires identifying the profile of oleotourists to determine sustainable strategies to increase demand without harming the local community. The objective of this study is to identify the Andalusian oleotourism offer according to the profile of oleotourists and project its demand evolution, in order to offer a sustainable product best suited to the demand. With this aim, three techniques are applied in this study: a random survey addressed to oleotourists in Andalusia, a SWOT (strengths, weaknesses, opportunities, and threats) analysis to evaluate the strengths and weaknesses of the oleotourism sector in the region, and finally, its demand is projected by using the ARIMA (autoregressive integrated moving average) model. The results indicate a favorable future scenario that should induce entrepreneurs and local authorities to invest in promoting and developing a product. Oleotourism is an alternative that can serve as a complement to agricultural income and generate employment.Junta de Andalucía proyecto SEJ-132Cátedra de Economía de la Energía y el Medio Ambiente de la Universidad de Sevilla Referencia: 1394/0103Ministerio de Educación de Chile Proyecto FONDECYT 115002

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.Peer ReviewedPostprint (author's final draft

Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level

Modelação da procura turística para Moçambique

O presente artigo teve como objetivo modelar e prever a procura turística em Moçambique, para o período compreendido entre Janeiro 2004 e Dezembro 2013, através do Método de Regressão Linear Múltipla. Para tal a variável número de dormidas, representando a procura turística, foi utilizada como variável dependente, explicada pelas variáveis Índice de Preços ao Consumidor (IPC), Produto Interno Bruto per capita (PIB) e Taxa de Câmbio (TC), para os mercados de Moçambique, Portugal, Reino Unido, Estados Unidos da América e África do Sul. Os resultados obtidos permitiram concluir que as variáveis IPC de Moçambique e TC face ao Metical da África do Sul, Portugal e Estados Unidos da América são estatisticamente significativas e explicam o comportamento da procura turística. Por outro lado, o modelo encontrado apresentou qualidades estatísticas e de ajustamento suficientes para explicar a procura turística.info:eu-repo/semantics/publishedVersio

Modelação da procura turística para Moçambique

O presente artigo teve como objetivo modelar e prever a procura turística em Moçambique, para o período compreendido entre Janeiro 2004 e Dezembro 2013, através do Método de Regressão Linear Múltipla. Para tal a variável número de dormidas, representando a procura turística, foi utilizada como variável dependente, explicada pelas variáveis Índice de Preços ao Consumidor (IPC), Produto Interno Bruto per capita (PIB) e Taxa de Câmbio (TC), para os mercados de Moçambique, Portugal, Reino Unido, Estados Unidos da América e África do Sul. Os resultados obtidos permitiram concluir que as variáveis IPC de Moçambique e TC face ao Metical da África do Sul, Portugal e Estados Unidos da América são estatisticamente significativas e explicam o comportamento da procura turística. Por outro lado, o modelo encontrado apresentou qualidades estatísticas e de ajustamento suficientes para explicar a procura turística.info:eu-repo/semantics/publishedVersio

Geo Data Science for Tourism

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.

Economics of Commercial Giant Clam Mariculture

Resource /Energy Economics and Policy,