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Developing tourism demand forecasting models using machine learning techniques with trend, seasonal, and cyclic components
12 references·0 citing·42 second hop
Developing tourism demand forecasting models using machine learning techniques with trend, seasonal, and cyclic components (2015)Developing tourism demand forecasting mod…A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis (2008)A decision support system for…Tourism demand modelling and forecasting—A review of recent research (2007)Tourism demand modelling and …Support vector regression with genetic algorithms in forecasting tourism demand (2006)Support vector regression wit…Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting (2006)Comparison of ARIMA, neural n…Neural network forecasting for seasonal and trend time series (2003)Neural network forecasting fo…Stock market trend prediction using ARIMA-based neural networks (2002)Stock market trend prediction…Combining neural network model with seasonal time series ARIMA model (2002)Combining neural network mode…Forecasting aggregate retail sales: (2001)Forecasting aggregate retail …Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting (2000)Back-propagation learning in …A neural network model to forecast Japanese demand for travel to Hong Kong (1999)A neural network model to for…Using a neural network to forecast visitor behavior (1996)Using a neural network to for…Neural Network Models for Time Series Forecasts (1996)Neural Network Models for Tim…Classifying Tourists into Market Segments (1992)Forecasting tourism demand: A review of empirical research (1995)An Econometric Classification and Review of International Tourism Demand Models (1997)Recent Developments in Econometric Modeling and Forecasting (2005)A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting (2019)AI in marketing, consumer research and psychology: A systematic literature review and research agenda (2021)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Averages of Forecasts: Some Empirical Results (1983)Error measures for generalizing about forecasting methods: Empirical comparisons (1992)The use of data mining and neural networks for forecasting stock market returns (2005)An Empirical Comparison of Machine Learning Models for Time Series Forecasting (2010)Time Series Analysis, Forecasting and Control. (1971)Book Review: Time Series Analysis: Forecasting and Control (1977)An Almost Ideal Demand System (1980)Social Media in Tourism and Hospitality: A Literature Review (2013)Progress and prospects for event tourism research (2015)Big data in tourism research: A literature review (2018)Trends and random walks in macroeconmic time series (1982)Forecasting with artificial neural networks: (1998)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Forecasting: theory and practice (2022)Forecasting with Univariate Box‐Jenkins Models (1983)Application of support vector machines in financial time series forecasting (2001)Artificial intelligence in tourism and hospitality: Bibliometric analysis and research agenda (2022)Forecasting methods and applications (1993)Supply Chain Management: Strategy, Planning and Operations (2000)Supply Chain Management: Strategy, Planning, and Operation (2003)A literature review and perspectives in reverse logistics (2015)Artificial intelligence in supply chain management: A systematic literature review (2020)Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities (2020)Combining kohonen maps with arima time series models to forecast traffic flow (1996)An artificial neural network (p,d,q) model for timeseries forecasting (2009)A novel hybridization of artificial neural networks and ARIMA models for time series forecasting (2010)Statistical methods versus neural networks in transportation research: Differences, similarities and some insights (2010)Comparing Predictive Accuracy (1994)Retail forecasting: Research and practice (2019)Time-series forecasting of seasonal items sales using machine learning – A comparative analysis (2022)Decision support and expert systems (1993)Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks (1998)Surveying stock market forecasting techniques – Part II: Soft computing methods (2008)Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques (2014)
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References

  • 2007Tourism demand modelling and forecasting—A review of recent research1,448 cited
  • 2003Neural network forecasting for seasonal and trend time series988 cited
  • 1996Neural Network Models for Time Series Forecasts477 cited
  • 2006Support vector regression with genetic algorithms in forecasting tourism demand383 cited
  • 2000Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting355 cited
  • 2002Combining neural network model with seasonal time series ARIMA model347 cited
  • 1999A neural network model to forecast Japanese demand for travel to Hong Kong309 cited
  • 2008A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis263 cited
  • 2001Forecasting aggregate retail sales:258 cited
  • 2002Stock market trend prediction using ARIMA-based neural networks183 cited
  • 2006Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting120 cited
  • 1996Using a neural network to forecast visitor behavior92 cited

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