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Forecasting accuracy evaluation of tourist arrivals
16 references·16 citing·39 second hopsampled
Forecasting accuracy evaluation of tourist arrivals (2017)Forecasting accuracy evaluation of touris…Automatic time series forecasting: the forecast package for R (2017)Automatic time series forecas…Investigating the influence of tourism on economic growth and carbon emissions: Evidence from panel analysis of the European Union (2013)Investigating the influence o…Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011)Forecasting Time Series With …Forecasting European industrial production with singular spectrum analysis (2008)Forecasting European industri…Automatic Time Series Forecasting: The forecast Package for R (2008)Automatic Time Series Forecas…Designing an artificial neural network for forecasting tourism time series (2005)Designing an artificial neura…A comparison of three different approaches to tourist arrival forecasting (2003)A comparison of three differe…Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention (2002)Modeling and forecasting tour…Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting (2000)Back-propagation learning in …Forecasting: Methods and Applications (1999)Forecasting: Methods and Appl…A neural network model to forecast Japanese demand for travel to Hong Kong (1999)A neural network model to for…Testing the equality of prediction mean squared errors (1997)Testing the equality of predi…Forecasting methods and applications (1993)Forecasting methods and appli…Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource (1989)Space-Time Modelling with Lon…SEASONALITY AND THE ORDER OF INTEGRATION FOR CONSUMPTION* (1988)SEASONALITY AND THE ORDER OF …Forecasting principles and practiceForecasting principles and pr…LSTM-Based Deep Learning Models for Long-Term Tourism Demand Forecasting (2022)LSTM-Based Deep Learning Mode…Using SARIMA–CNN–LSTM approach to forecast daily tourism demand (2021)Using SARIMA–CNN–LSTM approac…Tourism Demand Forecasting Based on an LSTM Network and Its Variants (2021)Tourism Demand Forecasting Ba…Forecasting air passenger numbers with a GVAR model (2021)Forecasting air passenger num…Using machine learning and big data for efficient forecasting of hotel booking cancellations (2020)Using machine learning and bi…Tourism Demand and the COVID-19 Pandemic: An LSTM Approach (2020)Tourism Demand and the COVID-…Machine Learning in Internet Search Query Selection for Tourism Forecasting (2020)Machine Learning in Internet …Group pooling for deep tourism demand forecasting (2020)Group pooling for deep touris…Forecasting Tourism Demand with an Improved Mixed Data Sampling Model (2020)Forecasting Tourism Demand wi…A novel two-step procedure for tourism demand forecasting (2020)A novel two-step procedure fo…A decomposition-ensemble approach for tourism forecasting (2020)A decomposition-ensemble appr…Spatial-temporal forecasting of tourism demand (2019)Spatial-temporal forecasting …Forecasting campground demand in US national parks (2019)Forecasting campground demand…Forecasting tourism demand with denoised neural networks (2018)Forecasting tourism demand wi…Forecasting global stock market implied volatility indices (2018)Forecasting global stock mark…A paired neural network model for tourist arrival forecasting (2018)A paired neural network model…Time Series Analysis: Forecasting and Control (1977)Industrial and business forecasting methods (1982)Forecasting tourism demand: A review of empirical research (1995)Support vector regression with genetic algorithms in forecasting tourism demand (2006)COVID–19’s Impact on Stock Prices Across Different Sectors—An Event Study Based on the Chinese Stock Market (2020)Forecasting tourism recovery amid COVID-19 (2021)Comparing Predictive Accuracy (1994)Comparing Predictive Accuracy (1995)25 years of time series forecasting (2006)Tests of Conditional Predictive Ability (2006)A Comprehensive Look at The Empirical Performance of Equity Premium Prediction (2007)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)Unit root tests in panel data: asymptotic and finite-sample properties (2002)Testing for unit roots in heterogeneous panels (2003)Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries? (2015)Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China (2016)Carbon dioxide (CO2) emissions and economic growth: A systematic review of two decades of research from 1995 to 2017 (2018)Another look at measures of forecast accuracy (2006)Forecasting Destination Weekly Hotel Occupancy with Big Data (2016)Structural combination of seasonal exponential smoothing forecasts applied to load forecasting (2018)Forecasting: theory and practice (2022)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Testing the null hypothesis of stationarity against the alternative of a unit root (1992)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Spatial statistics (1981)Statistical Methods for Data with Long-Range Dependence (1992)Geostatistical Space–Time Models: A Review (1999)Space–Time Covariance Functions (2005)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)The theta model: a decomposition approach to forecasting (2000)Improved supply chain management based on hybrid demand forecasts (2005)Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach (2014)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)The tourism forecasting competition (2010)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)M5 accuracy competition: Results, findings, and conclusions (2022)
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References

  • n.d.Forecasting principles and practice4,364 cited
  • 2008Automatic Time Series Forecasting: The forecast Package for R3,538 cited
  • 1993Forecasting methods and applications2,393 cited
  • 1997Testing the equality of prediction mean squared errors2,103 cited
  • 2011Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing1,055 cited
  • 2017Automatic time series forecasting: the forecast package for R978 cited
  • 2013Investigating the influence of tourism on economic growth and carbon emissions: Evidence from panel analysis of the European Union649 cited
  • 1999Forecasting: Methods and Applications626 cited
  • 1989Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource625 cited
  • 2002Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention424 cited
  • 2000Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting344 cited
  • 2003A comparison of three different approaches to tourist arrival forecasting322 cited
  • 1999A neural network model to forecast Japanese demand for travel to Hong Kong309 cited
  • 1988SEASONALITY AND THE ORDER OF INTEGRATION FOR CONSUMPTION*299 cited

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2005
Designing an artificial neural network for forecasting tourism time series
286 cited
  • 2008Forecasting European industrial production with singular spectrum analysis270 cited
  • Cited by

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    • 2020Machine Learning in Internet Search Query Selection for Tourism Forecasting106 cited
    • 2020A decomposition-ensemble approach for tourism forecasting99 cited
    • 2019Spatial-temporal forecasting of tourism demand90 cited
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    • 2019Forecasting campground demand in US national parks81 cited
    • 2020Group pooling for deep tourism demand forecasting78 cited
    • 2018Forecasting global stock market implied volatility indices58 cited
    • 2020A novel two-step procedure for tourism demand forecasting48 cited
    • 2021Forecasting air passenger numbers with a GVAR model47 cited
    • 2021Tourism Demand Forecasting Based on an LSTM Network and Its Variants45 cited
    • 2018A paired neural network model for tourist arrival forecasting43 cited
    • 2022LSTM-Based Deep Learning Models for Long-Term Tourism Demand Forecasting40 cited