Special Issue "Tourism Forecasting: Time-Series Analysis of World and Regional Data"
A special issue of Forecasting (ISSN 2571-9394).
Deadline for manuscript submissions: 30 November 2021.
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
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
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.
- 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