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Combining linear and nonlinear model in forecasting tourism demand
16 references·16 citing·29 second hopsampled
Combining linear and nonlinear model in forecasting tourism demand (2011)Combining linear and nonlinear model in f…Support vector regression with genetic algorithms in forecasting tourism demand (2006)Support vector regression wit…To combine or not to combine: selecting among forecasts and their combinations (2004)To combine or not to combine:…Time series forecasting using a hybrid ARIMA and neural network model (2003)Time series forecasting using…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…Combining neural network model with seasonal time series ARIMA model (2002)Combining neural network mode…Application of support vector machines in financial time series forecasting (2001)Application of support vector…Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting (2000)Back-propagation learning in …A Simple Nonparametric Test of Predictive Performance (1992)A Simple Nonparametric Test o…Combining forecasts: A review and annotated bibliography (1990)Combining forecasts: A review…Combining forecasts: A review and annotated bibliography (1989)Combining forecasts: A review…Averages of Forecasts: Some Empirical Results (1983)Averages of Forecasts: Some E…The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)The accuracy of extrapolation…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecas…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecas…The Combination of Forecasts (1969)The Combination of ForecastsTourism demand forecasting with spatiotemporal features (2022)Tourism demand forecasting wi…Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach (2021)Forecasting Tourism Demand Wi…Inbound tourism demand forecasting framework based on fuzzy time series and advanced optimization algorithm (2020)Inbound tourism demand foreca…Forecasting tourist arrivals using denoising and potential factors (2020)Forecasting tourist arrivals …Tourism forecasting: A review of methodological developments over the last decade (2018)Tourism forecasting: A review…Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model (2017)Improving daily occupancy for…Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan (2016)Using a Single Dendritic Neur…Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model (2016)Interval-valued time series f…Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion (2016)Forecasting financial time se…A novel hybrid model for tourist volume forecasting incorporating search engine data (2016)A novel hybrid model for tour…Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks (2015)Forecasting Natural Gas Price…A combination method for interval forecasting of agricultural commodity futures prices (2015)A combination method for inte…Tourism Demand Forecasting with Neural Network Models: Different Ways of Treating Information (2014)Tourism Demand Forecasting wi…A novel approach to model selection in tourism demand modeling (2014)A novel approach to model sel…Tourism demand forecasting using novel hybrid system (2013)Tourism demand forecasting us…A Comparative Analysis of Three Types of Tourism Demand Forecasting Models: Individual, Linear Combination and Non-linear Combination (2013)A Comparative Analysis of Thr…Expert judgment: Some necessary conditions and an example. (1974)The M3-Competition: results, conclusions and implications (2000)Chapter 4 Forecast Combinations (2006)Specification Tests in Econometrics (1978)Forecasting tourism demand: A review of empirical research (1995)Predictability of Stock Returns: Robustness and Economic Significance (1995)Introductory Econometrics for Finance (2008)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Forecasting with artificial neural networks: (1998)A hybrid ARIMA and support vector machines model in stock price forecasting (2004)A novel hybridization of artificial neural networks and ARIMA models for time series forecasting (2010)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Forecasting methods and applications (1993)Artificial neural network models for forecasting and decision making (1994)Neural Network Models for Time Series Forecasts (1996)Corn Cash Price Forecasting (2020)25 years of time series forecasting (2006)Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2009)Support vector machine with adaptive parameters in financial time series forecasting (2003)Forecasting stock market movement direction with support vector machine (2004)Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey (2009)Exponential smoothing: The state of the art (1985)Error measures for generalizing about forecasting methods: Empirical comparisons (1992)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Generalized autoregressive conditional heteroskedasticity (1986)Using daily stock returns (1985)Combination forecasts of output growth in a seven‐country data set (2004)Handbook of Economic Forecasting (2013)
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References

  • 1977Time Series Analysis: Forecasting and Control19,200 cited
  • 1977Time Series Analysis: Forecasting and Control6,446 cited
  • 2003Time series forecasting using a hybrid ARIMA and neural network model4,469 cited
  • 1969The Combination of Forecasts3,194 cited
  • 1989Combining forecasts: A review and annotated bibliography2,170 cited
  • 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition1,465 cited
  • 1990Combining forecasts: A review and annotated bibliography1,465 cited
  • 2001Application of support vector machines in financial time series forecasting1,270 cited
  • 1992A Simple Nonparametric Test of Predictive Performance897 cited
  • 1983Averages of Forecasts: Some Empirical Results622 cited
  • 2002Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention424 cited
  • 2006Support vector regression with genetic algorithms in forecasting tourism demand378 cited
  • 2002Combining neural network model with seasonal time series ARIMA model345 cited
  • 2000Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting344 cited
  • 2004To combine or not to combine: selecting among forecasts and their combinations

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325 cited
  • 2003A comparison of three different approaches to tourist arrival forecasting322 cited
  • Cited by

    • 2018Tourism forecasting: A review of methodological developments over the last decade161 cited
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    • 2014A novel approach to model selection in tourism demand modeling87 cited
    • 2016Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model78 cited
    • 2020Forecasting tourist arrivals using denoising and potential factors69 cited
    • 2022Tourism demand forecasting with spatiotemporal features69 cited
    • 2013A Comparative Analysis of Three Types of Tourism Demand Forecasting Models: Individual, Linear Combination and Non-linear Combination68 cited
    • 2016A novel hybrid model for tourist volume forecasting incorporating search engine data62 cited
    • 2016Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan61 cited
    • 2015Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks53 cited
    • 2016Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion49 cited
    • 2017Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model45 cited
    • 2020Inbound tourism demand forecasting framework based on fuzzy time series and advanced optimization algorithm43 cited
    • 2021Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach43 cited