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Forecasting tourism demand with denoised neural networks · Study

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ArticleForecasting tourism demand with denoised neural networks

Emmanuel Sirimal Silva2018

OA

Articlegreen2018

Forecasting tourism demand with denoised neural networks

Emmanuel Sirimal Silva · Hossein Hassani · Saeed M. Heravi · Xu Huang

133 citations·0/0 files stored·Annals of Tourism Research

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Forecasting Techniques and Applications Stock Market Forecasting Methods Time Series Analysis and Forecasting

Abstract

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Citations in this library

Stored only when both papers are in the corpus — 133 citations exist upstream.

References

1992Testing the null hypothesis of stationarity against the alternative of a unit root n.d.Forecasting principles and practice 2008Automatic Time Series Forecasting: The forecast Package for R 1993Forecasting methods and applications 1960Forecasting Sales by Exponentially Weighted Moving Averages 1997Testing the equality of prediction mean squared errors 2004Forecasting seasonals and trends by exponentially weighted moving averages 2018Big data in tourism research: A literature review

Cited by

2020The impact of COVID-19 on tourism sector in India 2021Using SARIMA–CNN–LSTM approach to forecast daily tourism demand 2019Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends 2020A decomposition-ensemble approach for tourism forecasting 2020Forecasting Tourism Demand with an Improved Mixed Data Sampling Model 2020Group pooling for deep tourism demand forecasting 2021Forecasting Daily Tourism Demand for Tourist Attractions with Big Data: An Ensemble Deep Learning Method 2022Demand forecasting based machine learning algorithms on customer information: an applied approach

Details

Year2018

Typearticle

Published2018-11-27

VenueAnnals of Tourism Research

ISSN-L0160-7383

Languageen

Citations133

Files0 public / 0

ShelfForecasting Techniques and Applications

Identifiers

doi10.1016/j.annals.2018.11.006

openalex_workW2902499798

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