Forecasting Visitor Arrivals at Tourist Attractions: A Time Series Framework with the N-BEATS for Sustainable Tourism DOI Open Access
Ke Xu,

Junli Zhang,

Junhao Huang

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(18), P. 8227 - 8227

Published: Sept. 21, 2024

Contemporary techniques built on deep learning technologies enable precise forecasting of tourism demand, particularly for the relaunch sustainable following COVID-19. We developed a novel framework to forecast visitor arrivals at tourist attractions in post-COVID-19 period. To this end, time-based data partitioning module was first pioneered. The N-BEATS algorithm with multi-step strategies then imported build system historical data. visualization curve fitting, metrics error measures, wide-range horizons, different segmentations, and Diebold–Mariano test verify robustness proposed model. empirically validated using 1604 daily volumes Jiuzhaigou from 1 January 2020 13 May 2024 1459 observations Mount Siguniang October 18 2024. model achieved an average MAPE 39.60% MAAPE 0.32, lower than five baseline models SVR, LSTM, ARIMA, SARIMA, TFT. results show that can accurately capture sudden variations or irregular changes observations. findings highlight importance improving destination management anticipatory planning latest time series approaches achieve visitation forecasts.

Language: Английский

Advancing harmful algal bloom predictions using chlorophyll-a as an Indicator: Combining deep learning and EnKF data assimilation method DOI Creative Commons
Ibrahim Busari, Debabrata Sahoo, Narendra N. Das

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 382, P. 125441 - 125441

Published: April 19, 2025

Language: Английский

Citations

1
Design of a Fractional-Order Environmental Toxin-Plankton System in Aquatic Ecosystems: A Novel Machine Predictive Expedition with Nonlinear Autoregressive Neuroarchitectures DOI

Muhammad Junaid Ali Asif Raja,

Amir Sultan, Chuan‐Yu Chang

et al.

Water Research, Journal Year: 2025, Volume and Issue: unknown, P. 123640 - 123640

Published: April 1, 2025

Language: Английский

Citations

0
Forecasting Visitor Arrivals at Tourist Attractions: A Time Series Framework with the N-BEATS for Sustainable Tourism DOI Open Access
Ke Xu,

Junli Zhang,

Junhao Huang

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(18), P. 8227 - 8227

Published: Sept. 21, 2024

Contemporary techniques built on deep learning technologies enable precise forecasting of tourism demand, particularly for the relaunch sustainable following COVID-19. We developed a novel framework to forecast visitor arrivals at tourist attractions in post-COVID-19 period. To this end, time-based data partitioning module was first pioneered. The N-BEATS algorithm with multi-step strategies then imported build system historical data. visualization curve fitting, metrics error measures, wide-range horizons, different segmentations, and Diebold–Mariano test verify robustness proposed model. empirically validated using 1604 daily volumes Jiuzhaigou from 1 January 2020 13 May 2024 1459 observations Mount Siguniang October 18 2024. model achieved an average MAPE 39.60% MAAPE 0.32, lower than five baseline models SVR, LSTM, ARIMA, SARIMA, TFT. results show that can accurately capture sudden variations or irregular changes observations. findings highlight importance improving destination management anticipatory planning latest time series approaches achieve visitation forecasts.

Language: Английский

Citations

0