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Attention-Based STL-BiLSTM Network to Forecast Tourist Arrival
16 references·1 citing·45 second hop
Attention-Based STL-BiLSTM Network to Forecast Tourist Arrival (2021)Attention-Based STL-BiLSTM Network to For…Tourism Demand Forecasting: A Decomposed Deep Learning Approach (2020)Tourism Demand Forecasting: A…Tourism demand forecasting: A deep learning approach (2019)Tourism demand forecasting: A…The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)The Performance of LSTM and B…A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting (2019)A review of research on touri…Big data in tourism research: A literature review (2018)Big data in tourism research:…Forecasting Destination Weekly Hotel Occupancy with Big Data (2016)Forecasting Destination Weekl…Forecasting city arrivals with Google Analytics (2016)Forecasting city arrivals wit…Forecasting Chinese tourist volume with search engine data (2014)Forecasting Chinese tourist v…Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach (2014)Can Google data improve the f…Predicting Hotel Demand Using Destination Marketing Organization’s Web Traffic Data (2013)Predicting Hotel Demand Using…Tourism economics research: A review and assessment (2012)Tourism economics research: A…Predicting the Present with Google Trends (2012)Predicting the Present with G…Tourism demand modelling and forecasting—A review of recent research (2007)Tourism demand modelling and …Recent Developments in Econometric Modeling and Forecasting (2005)Recent Developments in Econom…Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting (2000)Back-propagation learning in …Forecasting tourism demand: A review of empirical research (1995)Forecasting tourism demand: A…Forecasting tourism demand with a novel robust decomposition and ensemble framework (2023)Forecasting tourism demand wi…Time Series Analysis, Forecasting and Control. 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(1978)Time series analysis forecasting and control (2009)Bayesian BILSTM approach for tourism demand forecasting (2020)Forecasting tourism demand with multisource big data (2020)The good, the bad and the ugly on COVID-19 tourism recovery (2020)An Almost Ideal Demand System (1980)Forecasting with artificial neural networks: (1998)Review of tourism forecasting research with internet data (2020)Tourism Demand and the COVID-19 Pandemic: An LSTM Approach (2020)Forecasting tourism recovery amid COVID-19 (2021)Forecasting principles and practiceTime Series Analysis Forecasting and Control. (1971)Stock price prediction using deep learning and frequency decomposition (2020)Forecasting crude oil futures prices using BiLSTM-Attention-CNN model with Wavelet transform (2022)Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM (2022)In Search of Attention (2011)Text as Data (2019)
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References

  • 2012Predicting the Present with Google Trends2,353 cited
  • 2019The Performance of LSTM and BiLSTM in Forecasting Time Series1,735 cited
  • 2007Tourism demand modelling and forecasting—A review of recent research1,419 cited
  • 2018Big data in tourism research: A literature review1,073 cited
  • 1995Forecasting tourism demand: A review of empirical research896 cited
  • 2019A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting526 cited
  • 2005Recent Developments in Econometric Modeling and Forecasting524 cited
  • 2012Tourism economics research: A review and assessment487 cited
  • 2019Tourism demand forecasting: A deep learning approach451 cited
  • 2014Forecasting Chinese tourist volume with search engine data432 cited
  • 2000Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting344 cited
  • 2014Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach331 cited
  • 2013Predicting Hotel Demand Using Destination Marketing Organization’s Web Traffic Data234 cited
  • 2016Forecasting Destination Weekly Hotel Occupancy with Big Data229 cited
  • 2016

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Forecasting city arrivals with Google Analytics
187 cited
  • 2020Tourism Demand Forecasting: A Decomposed Deep Learning Approach179 cited
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    • 2023Forecasting tourism demand with a novel robust decomposition and ensemble framework38 cited