Tourism Demand Forecasting Using Nonadditive Forecast Combinations
Bibliographic Data
| ID | 21652776 |
|---|---|
| Authors | Yi‐Chung Hu (0000-0003-3090-1515), Geng Wu (0000-0002-1120-8638, Chung Yuan Christian University), Peng Jiang (0000-0003-4876-693X, Shandong University) |
| Year | 2023 |
| Volume | 47 |
| Issue | 5 |
| Pages | 775-799 |
| Publication date | 2023-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Hospitality & Tourism Research (JOURNAL) |
| Journal identifiers | ISSN: 1096-3480 • E-ISSN: 1557-7554 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/10963480211047857 |
| OpenAlex | W3201914360 |
| Language | EN |
| Citations received | 8 |
| References cited | 69 |
Accurately forecasting the demand for tourism can help governments formulate industrial policies and guide the business sector in investment planning. Combining forecasts can improve the accuracy of forecasting the demand for tourism, but limited work has been devoted to developing such combinations. This article addresses two significant issues in this context. First, the linear combination is the commonly used method of combining tourism forecasts. However, additive techniques unreasonably ignore interactions among the inputs. Second, the available data often do not adhere to specific statistical assumptions. Grey prediction has thus drawn attention because it does not require that the data follow any statistical distribution. This study proposes a nonadditive combination method by using the fuzzy integral to integrate single-model forecasts obtained from individual grey prediction models. Using China and Taiwan tourism demand as empirical cases, the results show that the proposed method outperforms the other combined methods considered here
Demand forecasting · Econometrics · Economics · Fuzzy logic · Operations research · Tourism · Computer Science · Energy Load and Power Forecasting · Engineering · Forecasting Techniques and Applications · Grey System Theory Applications · Mathematics · Artificial Intelligence
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| Unique citing works | 8 |
|---|---|
| Citations per year | 2.67 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 8 |