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Forecasting Tourist Arrivals via Random Forest and Long Short-term Memory
15 references·1 citing·47 second hopsampled
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References

  • 2017Deep learning with long short-term memory networks for financial market predictions2,827 cited
  • 2018Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models786 cited
  • 2018Forecasting tourist arrivals with machine learning and internet search index381 cited
  • 2017Big Data sources and methods for social and economic analyses343 cited
  • 2013Forecasting tourism demand to Catalonia: Neural networks vs. time series models270 cited
  • 2018Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index248 cited
  • 2016The Baidu Index: Uses in predicting tourism flows –A case study of the Forbidden City244 cited
  • 2015The influence of tourism website on tourists' behavior to determine destination selection: A case study of creative economy in Korea229 cited
  • 2011Forecasting tourism demand based on empirical mode decomposition and neural network217 cited
  • 2016Forecasting city arrivals with Google Analytics187 cited
  • 2016Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data139 cited
  • 2019The combination of interval forecasts in tourism91 cited
  • 2015Can internet searches forecast tourism inflows?87 cited
  • 2020Effective long short-term memory with fruit fly optimization algorithm for time series forecasting

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75 cited
  • 2016Seasonal SVR with FOA algorithm for single-step and multi-step ahead forecasting in monthly inbound tourist flow68 cited
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    • 2021Tourism demand forecasting and tourists’ search behavior: evidence from segmented Baidu search volume57 cited