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A novel approach to model selection in tourism demand modeling
Akın, Melda - In: Tourism management : research, policies, practice 48 (2015), pp. 64-73
Persistent link: https://www.econbiz.de/10010508442
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Forecasting US tourist arrivals using optimal singular spectrum analysis
Hassani, Hossein; Webster, Allan D.; Silva, Emmanuel Sirimal - In: Tourism management : research, policies, practice 46 (2015), pp. 322-335
Persistent link: https://www.econbiz.de/10010486219
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Combination forecasts of tourism demand with machine learning models
Claveria, Oscar; Monte, Enric; Torra, Salvador - In: Applied economics letters 23 (2016) 4/6, pp. 428-431
Persistent link: https://www.econbiz.de/10011430763
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Modelling tourism demand to Spain with machine learning techniques : the impact of forecast horizon on model selection
Claveria, Oscar; Torra, Salvador; Monte, Enric - In: Revista de economía aplicada : REA 24 (2016) 72, pp. 109-132
Persistent link: https://www.econbiz.de/10012261644
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Optimum prediction and forecasting of wheat demand in Iran
Babazadeh, Reza; Shamsi, Meisam; Shafipour, Fatemeh - In: International journal of applied management science : IJAMS 13 (2021) 2, pp. 141-151
Persistent link: https://www.econbiz.de/10012594613
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Winning methods for forecasting seasonal tourism time series
Brierley, Phil - In: International journal of forecasting 27 (2011) 3, pp. 853-854
Persistent link: https://www.econbiz.de/10009248093
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Forecasting tourism demand to Catalonia : neural networks vs. time series models
Claveria, Oscar; Torra, Salvador - In: Economic modelling 36 (2014), pp. 220-228
Persistent link: https://www.econbiz.de/10010412358
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Tourism demand forecasting for the tourism industry: a neural network approach
Law, Rob; Pine, Ray - In: Neural networks in business forecasting, (pp. 121-141). 2004
Persistent link: https://www.econbiz.de/10002349789
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Regional Tourism Demand Forecasting with Machine Learning Models : Gaussian Process Regression vs. Neural Network Models in a Multiple-Input Multiple-Output Setting
Claveria, Oscar - 2017
This study presents a multiple-input multiple-output (MIMO) approach for multi-step-ahead time series prediction with a Gaussian process regression (GPR) model. We assess the forecasting performance of the GPR model with respect to several neural network architectures. The MIMO setting allows...
Persistent link: https://www.econbiz.de/10012959523
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Tourism Demand Forecasting with Different Neural Networks Models
Claveria, Oscar - 2014
This paper aims to compare the performance of different Artificial Neural Networks techniques for tourist demand …
Persistent link: https://www.econbiz.de/10013045968