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Forecasting the number of inbound tourists with Google Trends
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Forecasting the number of inbound tourists with Google Trends (2019)Forecasting the number of inbound tourist…Oil financialization and volatility forecast: Evidence from multidimensional predictors (2019)Oil financialization and vola…Measuring the interdependence between investor sentiment and crude oil returns: New evidence from the CFTC's disaggregated reports (2019)Measuring the interdependence…Forecasting tourism demand using search query data: A hybrid modelling approach (2019)Forecasting tourism demand us…Developing a hierarchical system for energy corporate risk factors based on textual risk disclosures (2019)Developing a hierarchical sys…A novel cryptocurrency price trend forecasting model based on LightGBM (2018)A novel cryptocurrency price …Forecasting tourism demand with Google trends: Accuracy comparison of countries versus cities (2017)Forecasting tourism demand wi…A deep learning ensemble approach for crude oil price forecasting (2017)A deep learning ensemble appr…Ensemble Forecasting for Complex Time Series Using Sparse Representation and Neural Networks (2016)Ensemble Forecasting for Comp…A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data (2016)A dynamic linear model to for…Forecasting Chinese tourist volume with search engine data (2014)Forecasting Chinese tourist v…Predicting the Present with Google Trends (2012)Predicting the Present with G…Nowcasting with Google Trends in an Emerging Market (2011)Nowcasting with Google Trends…Tourism demand modelling and forecasting—A review of recent research (2007)The impact of financial development on carbon emissions: An empirical analysis in China (2011)A Big Data Analytics Method for Tourist Behaviour Analysis (2016)Forecasting tourism demand with composite search index (2016)Forecasting tourist arrivals with machine learning and internet search index (2018)Comparing Predictive Accuracy (1994)Approximately normal tests for equal predictive accuracy in nested models (2006)Tests of Conditional Predictive Ability (2006)Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data (2016)In pursuit of an effective B2B digital marketing strategy in an emerging market (2019)Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach (2020)Hierarchical Modeling and Analysis for Spatial Data (2003)Hierarchical Modeling and Analysis for Spatial Data (2014)Forecasting tourist arrivals at attractions: Search engine empowered methodologies (2018)Machine Learning in Internet Search Query Selection for Tourism Forecasting (2020)Winners and Losers from COVID-19: Global Evidence from Google Search (2020)Testing for a Unit Root in Time Series Regression (1988)Comparing Predictive Accuracy (1995)Stock price prediction based on deep neural networks (2019)A comprehensive evaluation of ensemble learning for stock-market prediction (2020)Price forecasting through neural networks for crude oil, heating oil, and natural gas (2024)Seasonal integration and cointegration (1990)Business Intelligence and Analytics: From Big Data to Big Impact (2012)Spurious patterns in Google Trends data - An analysis of the effects on tourism demand forecasting in Germany (2019)A systematic literature review on the use of big data for sustainable tourism (2021)Can volume predict Bitcoin returns and volatility? 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(2019)Assessing the extreme risk spillovers of international commodities on maritime markets: A GARCH-Copula-CoVaR approach (2020)Forecasting crude oil market volatility using variable selection and common factor (2022)Investor Sentiment and the Cross‐Section of Stock Returns (2006)Index Investment and the Financialization of Commodities (2012)On the network topology of variance decompositions: Measuring the connectedness of financial firms (2014)Dynamic and frequency-domain spillover among economic policy uncertainty, stock and housing markets in China (2019)Dynamic impacts of crude oil price on Chinese investor sentiment: Nonlinear causality and time-varying effect (2019)Oil shocks, competition, and corporate investment: Evidence from China (2020)In Search of Attention (2011)Big data in tourism research: A literature review (2018)Text as Data (2019)
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References

  • 2012Predicting the Present with Google Trends2,353 cited
  • 2018A novel cryptocurrency price trend forecasting model based on LightGBM518 cited
  • 2014Forecasting Chinese tourist volume with search engine data432 cited
  • 2017A deep learning ensemble approach for crude oil price forecasting383 cited
  • 2011Nowcasting with Google Trends in an Emerging Market286 cited
  • 2019Oil financialization and volatility forecast: Evidence from multidimensional predictors154 cited
  • 2017Forecasting tourism demand with Google trends: Accuracy comparison of countries versus cities127 cited
  • 2016A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data115 cited
  • 2019Measuring the interdependence between investor sentiment and crude oil returns: New evidence from the CFTC's disaggregated reports99 cited
  • 2019Developing a hierarchical system for energy corporate risk factors based on textual risk disclosures88 cited
  • 2019Forecasting tourism demand using search query data: A hybrid modelling approach70 cited
  • 2016Ensemble Forecasting for Complex Time Series Using Sparse Representation and Neural Networks60 cited

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