Chengyuan Zhang
Biographic Data
| ID | 6388488 |
|---|---|
| NAME | Chengyuan Zhang |
| GIVEN NAMES | Chengyuan |
| FAMILY NAME | Zhang |
| SIGNATURE | ZHANG C |
| AFFILIATIONS | Xidian University |
| ORCID | 0000-0001-8704-8856 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Spatio-temporal forecasting of tourism demand using graph Wavenet
Insights into space tourism discourse on TikTok using a deep visual model and a text mining method
Recently, the emergence of commercial space travel products has generated significant interest in space tourism from the public. The primary purpose of this study is to extensively explore the development trends, hot topics, and public attitudes of space tourism, offering broad implications for researchers and stakeholders both theoretically and practically. Thus, a total of 4727 short videos pertaining to space tourism on TikTok were collected. …
A novel deep learning approach for tourism volume forecasting with tourist search data
Tourism volume forecasting is the hot topic in tourism management, and deep learning techniques as the promising tool are becoming popular for capturing the characteristics of tourism volume data, which is reflected in two aspects: data dimension reduction (i.e., stacked autoencoders [SAE]) and model forecasting (i.e., bi‐directional gated recurrent unit neural network [Bi‐GRU]). With Hong Kong inbound arrivals as a case, this study has empirical…
A novel Bemd-based method for forecasting tourist volume with search engine data
As helpful big data, search engine data (SED) regarding tourism-related factors have currently been introduced to tourist volume prediction, but they have been shown to impact the tourism market on different timescales (or frequency band). This study develops a novel forecasting method using an emerging multiscale analysis—bivariate empirical mode decomposition (BEMD)—to investigate multiscale relationships. Three major steps are performed: (1) S…
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A novel Bemd-based method for forecasting tourist volume with search engine data
As helpful big data, search engine data (SED) regarding tourism-related factors have currently been introduced to tourist volume prediction, but they have been shown to impact the tourism market on different timescales (or frequency band). This study develops a novel forecasting method using an emerging multiscale analysis—bivariate empirical mode decomposition (BEMD)—to investigate multiscale relationships. Three major steps are performed: (1) S…
A novel deep learning approach for tourism volume forecasting with tourist search data
Tourism volume forecasting is the hot topic in tourism management, and deep learning techniques as the promising tool are becoming popular for capturing the characteristics of tourism volume data, which is reflected in two aspects: data dimension reduction (i.e., stacked autoencoders [SAE]) and model forecasting (i.e., bi‐directional gated recurrent unit neural network [Bi‐GRU]). With Hong Kong inbound arrivals as a case, this study has empirical…
Insights into space tourism discourse on TikTok using a deep visual model and a text mining method
Recently, the emergence of commercial space travel products has generated significant interest in space tourism from the public. The primary purpose of this study is to extensively explore the development trends, hot topics, and public attitudes of space tourism, offering broad implications for researchers and stakeholders both theoretically and practically. Thus, a total of 4727 short videos pertaining to space tourism on TikTok were collected. …
Spatio-temporal forecasting of tourism demand using graph Wavenet
Diverse Aspects of Tourism Research (4 works) · Tourism (4 works) · Computer Science (3 works) · Geography (3 works) · Data mining (2 works) · Data science (2 works) · Machine learning (2 works) · Mathematics (2 works) · Wine Industry and Tourism (2 works) · Archaeology (1 work)