Generation of ensemble forecasts using functional-link net for decomposition ensemble learning to forecast tourist arrivals

Yi-Chung Hu (), Geng Wu () and Mei-Ling Wu ()
Additional contact information
Yi-Chung Hu: Chung Yuan Christian University
Geng Wu: Ningbo University of Technology
Mei-Ling Wu: Chung Yuan Christian University

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 5, No 12, 4159-4184

Abstract: Abstract Decomposition and ensemble approaches have come to play an increasingly significant role in the tourism literature. Nonlinear artificial intelligence (AI) methods, including the multi-layer perceptron and support vector regression, have been widely employed to predict the demand for tourism based on decomposition and ensemble learning. However, even though the effectiveness of functional-link net (FLN) with a flat network structure at function approximation, few of studies are devoted to the use of FLN to tourist arrivals forecasting. The main issue we address is to investigate the effect of combining different decomposition methods with FLN on the forecasting performance of decomposition ensemble models. This study features the development of decomposition ensemble models to forecast the tourist arrivals by using commonly used AI methods to forecast the series of individual modes after decomposing data into several subsequences, and FLN was then applied to generate ensemble forecasts. We used the inbound tourist arrivals to Taiwan to verify the performance of the proposed decomposition ensemble methods. It was found that they significantly outperformed commonly used benchmark models.

Keywords: Decomposition ensemble; Neural network; Artificial intelligence; Functional link; Tourist arrivals (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11135-025-02181-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:59:y:2025:i:5:d:10.1007_s11135-025-02181-z

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-025-02181-z

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().