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

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

Deadline for manuscript submissions: 1 May 2022.

Special Issue Editors

Prof. Dr. João Paulo Ramos Teixeira
E-Mail
Guest Editor
Research Centre in Digitalization and Intelligent Robotics (CEDRI), and Applied Management Research Unit (UNIAG) 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
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 papers will be 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 1000 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

Published Papers (2 papers)

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Research

Article
Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests
Forecasting 2021, 3(4), 884-919; https://doi.org/10.3390/forecast3040054 (registering DOI) - 27 Nov 2021
Viewed by 307
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 [...] Read more.
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)
Article
COVID-19 and Tourism: Analyzing the Effects of COVID-19 Statistics and Media Coverage on Attitudes toward Tourism
Forecasting 2021, 3(4), 870-883; https://doi.org/10.3390/forecast3040053 - 17 Nov 2021
Viewed by 477
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 [...] Read more.
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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