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Special Issue "Tourism Forecasting: Time-Series Analysis of World and Regional Data"

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A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Economics and Management".

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 23941

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Special Issue Editors

Prof. Dr. João Paulo Ramos Teixeira

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Guest Editor

Research Centre in Digitalization and Intelligent Robotics (CEDRI) - Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
Interests: speech synthesis; prosody; speech systems; modulation; prediction with neural networks; DNN; LSTM; time series forecast and biological signals analysis; namely EEG; ECG and voice
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ulrich Gunter

E-Mail Website
Guest Editor

Department of Tourism and Service Management, MODUL University Vienna, 1190 Vienna, Austria
Interests: tourism demand forecasting; time series analysis and forecasting; panel data analysis; applied econometrics; tourism economics; sustainability

Special Issue Information

Dear Colleagues,

Tourism and its time series forecast are of high importance for regional and national economies all over the world. Their importance has become even more evident in a period when demand suffered an abrupt rupture due to SARS-CoV-2.

With the COVID-19 vaccination now in sight, the recovery of the tourism sector is expected. More than ever, accurate forecasting of tourism demand at all levels is of the utmost importance for investors, local and national political decision-makers to prepare the infrastructures, investments, and operator recruitment to receive tourists.

The aim of this Special Issue is to collect contributions about analysis and forecasting tourism time series before, during, and after the pandemic period.

We are pleased to invite you to submit your valuable contributions in the main scope of the Forecasting journal and devoted to tourism forecasting. Global, national, and regional data analysis are welcomed, in addition to the sectorial tourism analysis (transportation, accommodation, domestic tourism, senior tourism, health tourism, scientific tourism, etc.). All forecasting methods devoted to tourism time series sectors are welcome. Contributions considering the COVID-19 pandemic period analysis and the recovery period for the tourism sector forecast, considering similar or different opening scenarios are in the scope of this Special Issue.

For this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Analysis of tourism time series;
Forecasting of tourism time series using linear and non-linear models;
Univariate and multivariate models;
Statistical, machine learning, and hybrid models;
Limitations and possibilities of forecasting in the light of the COVID-19 pandemic;
Scenario forecasting;
Point, interval, and density forecasting;
Big data as leading indicators in the COVID-19 pandemic;
Forecast combination;
Directional change accuracy;
Ex-ante tourism demand forecasting;
Forecasting for single attractions, tourism segments, the sharing economy, etc.

Prof. Dr. João Paulo Ramos Teixeira
Prof. Dr. Ulrich Gunter
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Tourism Demand
Forecasting Models
Tourism Time Series Analysis
Tourism Time Series Forecast
Tourism Prediction
Tourism Recovery Forecast
Tourism under COVID-19 Pandemic
Decision Support
Tourism Analysis

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Editorial

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Open AccessEditorial

Editorial for Special Issue: “Tourism Forecasting: Time-Series Analysis of World and Regional Data”

João Paulo Teixeira

and

Ulrich Gunter

Forecasting 2023, 5(1), 210-212; https://doi.org/10.3390/forecast5010011 - 02 Feb 2023

Viewed by 1080

Abstract

This Special Issue was honored with six contribution papers embracing the subject of tourism forecasting [...] Full article

(This article belongs to the Special Issue Tourism Forecasting: Time-Series Analysis of World and Regional Data)

Research

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Open AccessArticle

Forecasting Daily and Weekly Passenger Demand for Urban Rail Transit Stations Based on a Time Series Model Approach

Dung David Chuwang

and

Weiya Chen

Forecasting 2022, 4(4), 904-924; https://doi.org/10.3390/forecast4040049 - 16 Nov 2022

Cited by 3 | Viewed by 1652

Abstract

Forecasting daily and weekly passenger demand is a key fundamental process used by existing urban rail transit (URT) station authorities to diagnose operational problems and make decisions about train schedule patterns to improve operational efficiency, increase revenue management, and improve driving safety. The accuracy of the forecast results will directly affect the operation planning of urban rail transit (URT). Therefore, based on the collected inbound historical passenger data, this study used the Box–Jenkins time series with the Facebook Prophet algorithm to analyze the characteristics of urban rail transit passenger demand and achieved better computational forecasting performance accuracy. After analyzing the periodicity, correlation, and stationarity, different time series models were constructed. The Akaike information criteria (AIC), Bayesian information criteria (BIC), mean squared error (MSE), and root mean squared error (RMSE) were used to evaluate the adequacy of the best forecast model from among several tested candidates’ models for the Box–Jenkins. The parameters of the daily and weekly models were estimated using statistical software. The experimental results of this study are of both theoretical and practical significance to the urban rail transit (URT) station authorities for an effective station planning system. The forecasting results signify that the SARIMA (5, 1, 3) (1, 0, 0)²⁴ model performs better and is more stable in forecasting the daily passenger demand, and the ARMA (2, 1) model performs better in forecasting the weekly passenger demand. When comparing the SARIMA and ARMA models with the Facebook Prophet, results show that the Facebook Prophet model is superior to the SARIMA model for the daily time series, and the ARMA model is superior to the Facebook Prophet model for the weekly time series. Full article

(This article belongs to the Special Issue Tourism Forecasting: Time-Series Analysis of World and Regional Data)

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Open AccessArticle

The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai

Ahmed Shoukry Rashad

Forecasting 2022, 4(3), 674-684; https://doi.org/10.3390/forecast4030036 - 21 Jul 2022

Cited by 4 | Viewed by 2625

Abstract

Tourism plays an important economic role for many economies and after the COVID-19 pandemic, accurate tourism forecasting become critical for policymakers in tourism-dependent economies. This paper extends the growing literature on the use of internet search data in tourism forecasting through evaluating the predictive ability of Destination Insight with Google, a new Google product designed to monitor tourism recovery after the COVID-19 pandemic. This paper is the first attempt to explore the forecasting ability of the new Google data. The study focuses on the case of Dubai, given its status as a world-leading tourism destination. The study uses time series models that account for seasonality, trending variables, and structural breaks. The study uses monthly data for the period of January 2019 to April 2022. We explore whether the internet travel search queries can improve the forecasting of tourist arrivals to Dubai from the UK. We evaluate the accuracy of forecasts after incorporating the Google variable in our model. Our findings suggest that the new Google data can significantly improve tourism forecasting and serves as a leading indicator of tourism demand. Full article

(This article belongs to the Special Issue Tourism Forecasting: Time-Series Analysis of World and Regional Data)

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Open AccessArticle

Forecasting Regional Tourism Demand in Morocco from Traditional and AI-Based Methods to Ensemble Modeling

El houssin Ouassou

and

Hafsa Taya

Forecasting 2022, 4(2), 420-437; https://doi.org/10.3390/forecast4020024 - 06 Apr 2022

Cited by 4 | Viewed by 3714

Abstract

Tourism is one of the main sources of wealth for the Moroccan regions, since, in 2019, it contributed 7.1% to the total GDP. However, it is considered to be one of the sectors most vulnerable to exogenous shocks (political and social stability, currency change, natural disasters, pandemics, etc.). To control this, policymakers tend to use various techniques to forecast tourism demand for making crucial decisions. In this study, we aimed to forecast the number of tourist arrivals to the Marrakech-Safi region using annual data for the period from 1999 to 2018 by using three conventional approaches (ARIMA, AR, and linear regression), and then we compared the results with three artificial intelligence-based techniques (SVR, XGBoost, and LSTM). Then, we developed hybrid models by combining both the conventional and AI-based models, using the technique of ensemble learning. The findings indicated that the hybrid models outperformed both conventional and AI-based techniques. It is clear from the results that using hybrid models can overcome the limitations of each method individually. Full article

(This article belongs to the Special Issue Tourism Forecasting: Time-Series Analysis of World and Regional Data)

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Open AccessArticle

Analyzing and Forecasting Tourism Demand in Vietnam with Artificial Neural Networks

Le Quyen Nguyen

Paula Odete Fernandes

and

João Paulo Teixeira

Forecasting 2022, 4(1), 36-50; https://doi.org/10.3390/forecast4010003 - 28 Dec 2021

Cited by 10 | Viewed by 5400

Abstract

Vietnam has experienced a tourism expansion over the last decade, proving itself as one of the top tourist destinations in Southeast Asia. The country received more than 18 million international tourists in 2019, compared to only 1.5 million twenty-five years ago. Tourist spending has translated into rising employment and incomes for Vietnam’s tourism sector, making it the key driver to the socio-economic development of the country. Following the COVID-19 pandemic, only 3.8 million international tourists visited Vietnam in 2020, plummeting by 78.7% year-on-year. The latest outbreak in early summer 2021 made the sector continue to hit bottom. Although Vietnam’s tourism has suffered extreme losses, once the contagion is under control worldwide, the number of international tourists to Vietnam is expected to rise again to reach pre-pandemic levels in the next few years. First, the paper aims to provide a summary of Vietnam’s tourism characteristics with a special focus on international tourists. Next, the predictive capability of artificial neural network (ANN) methodology is examined with the datasets of international tourists to Vietnam from 2008 to 2020. Some ANN architectures are experimented with to predict the monthly number of international tourists to the country, including some lockdown periods due to the COVID-19 pandemic. The results show that, with the correct selection of ANN architectures and data from the previous 12 months, the best ANN models can be forecast for next month with a MAPE between 7.9% and 9.2%. As the method proves its forecasting accuracy, it would serve as a valuable tool for Vietnam’s policymakers and firm managers to make better investment and strategic decisions. Full article

(This article belongs to the Special Issue Tourism Forecasting: Time-Series Analysis of World and Regional Data)

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Open AccessArticle

Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests

Ulrich Gunter

Forecasting 2021, 3(4), 884-919; https://doi.org/10.3390/forecast3040054 - 27 Nov 2021

Cited by 6 | Viewed by 2721

Abstract

The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal naïve forecast of the inflation-adjusted ADR) are employed. Forecast evaluation is carried out for forecast horizons h = 1, 7, 30, and 90 days ahead based on rolling windows. After conducting forecast encompassing tests, (a) mean, (b) median, (c) regression-based weights, (d) Bates–Granger weights, and (e) Bates–Granger ranks are used as forecast combination techniques. In the relative majority of cases (i.e., in 13 of 28), combined forecasts based on Bates–Granger weights and on Bates–Granger ranks provide the highest level of forecast accuracy in terms of typical measures. Finally, the employed methodology represents a fully replicable toolkit for practitioners in terms of both forecast models and forecast combination techniques. Full article

(This article belongs to the Special Issue Tourism Forecasting: Time-Series Analysis of World and Regional Data)

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Open AccessArticle

COVID-19 and Tourism: Analyzing the Effects of COVID-19 Statistics and Media Coverage on Attitudes toward Tourism

and

Forecasting 2021, 3(4), 870-883; https://doi.org/10.3390/forecast3040053 - 17 Nov 2021

Cited by 12 | Viewed by 4638

Abstract

COVID-19 has significantly influenced tourism, including tourists’ and residents’ attitudes toward tourism. At the same time, attitudes and consumer confidence are important for economic recovery in the tourism sector. This study explores the effects of the COVID-19 pandemic on people’s attitudes toward tourism by analyzing time-series data on the number of COVID-19 positive cases, vaccinations, news sentiment, a total number of daily mentions of tourism, and the share of voice for positive and negative sentiment toward tourism. The applied data analysis techniques include descriptive analysis, visual representation of data, data decomposition into trend and cycle components, unit root tests, Granger causality test, and multiple time series regression. The results demonstrate that the COVID-19 statistics and media coverage have significant effects on interest in tourism in general, as well as the positive and negative sentiment toward tourism. The results contribute to knowledge and practice by describing the effects of the disease statistics on attitudes toward tourism, introducing social media sentiment analysis as an opportunity to measure positive and negative sentiment toward tourism, and providing recommendations for government authorities, destination management organizations, and tourism providers. Full article

(This article belongs to the Special Issue Tourism Forecasting: Time-Series Analysis of World and Regional Data)

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