Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19

An innovative ADE-TFT interpretable tourism demand forecasting model was proposed to address the issue of the insufficient interpretability of existing tourism demand forecasting. This model effectively optimizes the parameters of the Temporal Fusion Transformer (TFT) using an adaptive differential...

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Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Jg. 53; H. 11; S. 14493 - 14514
Hauptverfasser:	Wu, Binrong, Wang, Lin, Zeng, Yu-Rong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2023 Springer Nature B.V
Schlagworte:	Adaptive algorithms Artificial Intelligence Artificial neural networks Computer Science Coronaviruses COVID-19 Demand analysis Dirichlet problem Evolutionary algorithms Evolutionary computation Forecasting Machine learning Machines Manufacturing Mathematical models Mechanical Engineering Pandemics Performance prediction Processes Search engines Tourism Deep learning Text mining COVID-19 Interpretable tourism demand forecasting
ISSN:	0924-669X, 1573-7497, 1573-7497
Online-Zugang:	Volltext
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