Forecasting tourism demand with denoised neural networks
Datos Bibliográficos
| ID | 11233992 |
|---|---|
| Autores | Emmanuel Sirimal Silva (0000-0003-3851-9230, University of the Arts London, autor de correspondencia), Hamidreza Hassani (0000-0003-0897-8663, University of Tehran), Saeed Heravi (0000-0002-0198-764X, Cardiff University), Xu Huang (0000-0003-3472-6822, De Montfort University) |
| Año | 2019 |
| Volumen | 74 |
| Páginas | 134-154 |
| Fecha de publicación | 2019-01-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Annals of Tourism Research (JOURNAL) |
| Identificadores de la revista | ISSN: 0160-7383 • E-ISSN: 1873-7722 |
| Editorial | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.annals.2018.11.006 |
| OpenAlex | W2902499798 |
| Idioma | EN |
| Citas recibidas | 34 |
| Referencias citadas | 45 |
Artificial neural network · Autoregressive integrated moving average · Autoregressive model · Autoregressive–moving-average model · Benchmark (surveying · Demand forecasting · Econometrics · Economics · Geography · Machine learning · Moving average · Noise (video · Nonparametric statistics · Operations management · Parametric statistics · Singular spectrum analysis · Statistics · Time series · Tourism · Computer Science · Energy Load and Power Forecasting · Forecasting Techniques and Applications · Grey System Theory Applications · Mathematics · Artificial Intelligence
Daily tourism demand forecasting before and during Covid-19
Forecasting daily tourism volume
A novel two-step procedure for tourism demand forecasting
Identifying the role of media discourse in tourism demand forecasting
Leveraging large language models for daily tourist demand forecasting
Is AI really better than conventional methods to identify the key drivers of firms’ performance? An exploratory study in the hospitality industry
Tourism demand with subtle seasonality
Unveiling fluctuation patterns of tourist volume
International tourism demand forecasting with machine learning models
Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence
A novel two-stage combination model for tourism demand forecasting
Fine-grained tourism demand forecasting
Multi‐horizon accommodation demand forecasting
Diversification in the tourism sector and economic growth in Australia
Monthly Tourism Demand Forecasting With Covid ‐19 Impact‐Based Hybrid Convolution Neural Network and Gate Recurrent Unit
Tourism impact assessment modeling of vegetation density for protected areas using data mining techniques
Forecasting inbound tourist arrivals to Iran post-Covid-19 pandemic
Enhancing tourism demand forecasting with two-stage feature selection and attention-augmented deep learning models
Regional tourism demand forecasting with spatiotemporal interactions
Tourism demand forecasting from the perspective of mobility
Forecast without historical data
Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid Covid-19? Mixed-data sampling approach
Google Trends and Baidu index data in tourism demand forecasting
The impact of Covid-19 on tourism sector in India
Bayesian BILSTM approach for tourism demand forecasting
Forecasting air passenger numbers with a GVAR model
Daily tourism volume forecasting for tourist attractions
Forecasting tourism growth with State-Dependent Models
Predictivity of tourism demand data
Tourism demand forecasting with time series imaging
A decomposition-ensemble approach for tourism forecasting
Group pooling for deep tourism demand forecasting
An Interval Decomposition-Ensemble Model for Tourism Forecasting
Googling Fashion
Automatic Time Series Forecasting
New developments in tourism and hotel demand modeling and forecasting
Testing the equality of prediction mean squared errors
Testing the null hypothesis of stationarity against the alternative of a unit root
Big data in tourism research
Forecasting Chinese tourist volume with search engine data
Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach
Forecasting tourism demand with composite search index
A comparison of three different approaches to tourist arrival forecasting
A neural network model to forecast Japanese demand for travel to Hong Kong
Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting
Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis
Forecasting accuracy evaluation of tourist arrivals
Cross country relations in European tourist arrivals
Forecasting in a Mixed Up World
| Obras citantes distintas | 34 |
|---|---|
| Citas por año | 4,86 |
| Intervalo de citas | 2019 - 2026 (8) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 34 |