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Using SARIMA–CNN–LSTM approach to forecast daily tourism demand · Study

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ArticleUsing SARIMA–CNN–LSTM approach to forecast daily tourism demand

Kaijian He2021

Article2021

Using SARIMA–CNN–LSTM approach to forecast daily tourism demand

Kaijian He · Don Chi Wai Wu · Kwok Fai Tso

130 citations·0/0 files stored·Journal of Hospitality and Tourism Management

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

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References

2017Deep learning with long short-term memory networks for financial market predictions 2006Approximately normal tests for equal predictive accuracy in nested models 1997Testing the equality of prediction mean squared errors 2007Tourism demand modelling and forecasting—A review of recent research 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2018Time series forecasting of petroleum production using deep LSTM recurrent networks 2018Financial time series forecasting model based on CEEMDAN and LSTM 2019A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

Cited by

2023A bi‐level ensemble learning approach to complex time series forecasting: Taking exchange rates as an example 2023A time series attention mechanism based model for tourism demand forecasting 2024A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting 2025An innovative method for improving hotel service using implicit requirement generation and quality function deployment

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Details

Year2021

Typearticle

Published2021-09-04

VenueJournal of Hospitality and Tourism Management

ISSN-L1447-6770

Languageen

Citations130

Files0 public / 0

ShelfForecasting Techniques and Applications

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doi10.1016/j.jhtm.2021.08.022

openalex_workW3196345400

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