Tourism demand forecasting
A novel multi-channel imaging model
Bibliographic Data
| ID | 19554199 |
|---|---|
| Authors | Yihong Chen (0000-0001-5176-4551), Tao Hu (0000-0001-7893-096X), Robin Law (0000-0001-7199-3757), Rob Law |
| Year | 2025 |
| Publication date | 2025-04-10 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Tourism Futures (JOURNAL) |
| Journal identifiers | ISSN: 2055-5911 • E-ISSN: 2055-592X |
| Publisher | Emerald (PUBLISHER) |
| DOI | 10.1108/jtf-12-2024-0263 |
| OpenAlex | W4409308786 |
| Language | EN |
| Citations received | 1 |
| References cited | 43 |
Purpose This study aims to introduce an innovative multi-channel imaging technique aimed at mitigating deep learning overfitting and facilitating the automatic extraction of features from limited 1D data. Design/methodology/approach The proposed framework consists of five key component: dimensionality reduction, sequence image generation, image stitching, feature extraction and model training. It converts 1D multi-temporal data into multiple 2D images utilizing Markov transition field, Gramian angular field and recurrence plot. These single-channel images are stitched into a larger n-channel image, which is processed by a convolutional neural network for feature extraction and forecasted using a long short-term memory network. Findings The results demonstrate that the proposed multi-channel imaging technique outperforms all benchmark models. This conversion captures underlying patterns and enhances information transmission. Additionally, models with multi-time series configurations perform better than single-time series setups, highlighting that data are more crucial than advanced models in forecasting. Originality/value This pioneering study explores the role of non-economic variables in tourism forecasting. The proposed multi-channel time series imaging model not only applies to tourism but also offers potential for interdisciplinary applications
Demand forecasting · Geography · Meteorology · Operations research · Telecommunications · Tourism · Computer Science · Diverse Aspects of Tourism Research · Energy Load and Power Forecasting · Engineering · Environmental Science · Wine Industry and Tourism
Long Short-Term Memory
Identifying the role of media discourse in tourism demand forecasting
Google Trends data and transfer function models to predict tourism demand in Italy
Advancing tourism demand forecasting in Sri Lanka
Hybrid SVR-Sarima model for tourism forecasting using PROMETHEE II as a selection methodology
Tourism demand forecasting using complex network theory
Tourism demand modelling and forecasting—A review of recent research
Forecasting international city tourism demand for Paris
Hierarchical pattern recognition for tourism demand forecasting
Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention
The relationship between vacation factors and socio-demographic and travelling characteristics
Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index
Tourism, seasonality and social change
Density tourism demand forecasting revisited
Exploring impact of climate on tourism demand
Attitude determinants in tourism destination choice
Tourism demand forecasting with time series imaging
Tourism demand forecasting with online news data mining
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |