About: A decomposition-ensemble approach for tourism forecasting

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A decomposition-ensemble approach for tourism forecasting
Creator	Qian, Yatong Wang, Shouyang Xie, Gang
Source	Elsevier; Medline; PMC
abstract	Abstract With the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly investigates how data characteristic analysis is used in a decomposition-ensemble approach. The empirical results show that the proposed model outperforms other models in both point and interval forecasts for different prediction horizons, indicating the effectiveness of the proposed approach for forecasting tourism demand, especially for time series with complexity.
has issue date	2020-03-31(xsd:dateTime)
bibo:doi	10.1016/j.annals.2020.102891
bibo:pmid	32501311
has license	els-covid
sha1sum (hex)	cfd000444a29f11bd63058a1097ea26098f82106
schema:url	https://doi.org/10.1016/j.annals.2020.102891
resource representing a document's title	A decomposition-ensemble approach for tourism forecasting
has PubMed Central identifier	PMC7147863
has PubMed identifier	32501311
schema:publication	Annals of Tourism Research
resource representing a document's body	covid:cfd000444a29f11bd63058a1097ea26098f82106#body_text
is schema:about of	named entity 'HOW' named entity 'CHARACTERISTIC' named entity 'BASED' named entity 'MODEL' named entity 'EMPIRICAL' named entity 'CASE' named entity 'RESULTS' named entity 'PREDICTION' named entity 'NOISE' named entity 'AREAS' named entity 'ENSEMBLE' named entity 'INDUSTRY' named entity 'PREDICTIVE' named entity 'EFFECTIVENESS' named entity '27S' named entity 'FORECASTING' named entity 'TOURISM' named entity 'APPROACH' named entity 'FORECASTING' named entity 'HONG KONG' named entity 'SUFFERED' named entity 'ADAPTIVE' named entity 'MODELS' named entity 'TIME SERIES' named entity 'INTERVAL' named entity 'OCCURRENCE' named entity 'PROPOSED' named entity 'COMPLETE' named entity 'ENSEMBLE' named entity 'FREQUENT' named entity 'INVESTIGATES' named entity 'DECOMPOSITION' named entity 'TOURISM' named entity 'APPROACH' named entity 'DIFFERENT' named entity 'RECENT' named entity 'ENHANCE' named entity 'EVENTS' named entity 'COMPLEXITY' named entity 'ACCURACY' named entity 'MODE' named entity 'GREAT' named entity 'NEURAL NETWORK MODEL' named entity 'USING' named entity 'FIRSTLY' named entity 'INDICATING' named entity 'VOLATILITY' named entity 'ANALYSIS' named entity 'STUDY' named entity 'POINT' named entity 'DEMAND' named entity 'USED' named entity 'YEARS' named entity 'IRREGULAR' named entity 'DECOMPOSITION' named entity 'DATA' covid:arg/cfd000444a29f11bd63058a1097ea26098f82106 named entity 'demand forecasting' named entity 'empirical mode decomposition' named entity 'Hong Kong' named entity 'interval forecasts' named entity 'neural network model' named entity '1, 2' named entity 'ARIMA' named entity 'SARS epidemic' named entity 'Ensemble forecast' named entity 'sample entropy' named entity 'forecasting accuracy' named entity 'Hong Kong' named entity 'complex time series' named entity 'tourist arrivals' named entity 'complex time series' named entity 'ith' named entity 'decision-making' named entity 'time series' named entity 'tourist arrivals' named entity 'time series' named entity 'marketing strategies' named entity 'Australia'

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