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Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models
16 references·0 citing·45 second hop
Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models (2023)Short-term forecasting airport passenger …Deep learning models for forecasting aviation demand time series (2021)Deep learning models for fore…Risks, resilience, and pathways to sustainable aviation: A COVID-19 perspective (2020)Risks, resilience, and pathwa…Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning (2019)Trend Prediction Classificati…Nonlinear vector auto-regression neural network for forecasting air passenger flow (2019)Nonlinear vector auto-regress…Forecasting air passenger demand with a new hybrid ensemble approach (2019)Forecasting air passenger dem…Forecasting short-term air passenger demand using big data from search engine queries (2016)Forecasting short-term air pa…Golden rule of forecasting: Be conservative (2015)Golden rule of forecasting: B…Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches (2014)Short-term forecasting of air…A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting (2014)A neuro-fuzzy combination mod…Machine Learning Strategies for Time Series Forecasting (2013)Machine Learning Strategies f…Forecasting of Hong Kong airport's passenger throughput (2013)Forecasting of Hong Kong airp…Predicting the Present with Google Trends (2012)Predicting the Present with G…A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application (2008)A multivariate neural forecas…Gravity models for airline passenger volume estimation (2007)Gravity models for airline pa…Model-Dependent Variance Inflation Factor Cutoff Values (2002)Model-Dependent Variance Infl…Comparing Predictive Accuracy (2002)Comparing Predictive AccuracyThe accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Combining forecasts: A review and annotated bibliography (1989)The M3-Competition: results, conclusions and implications (2000)Integrating human judgement into quantitative forecasting methods: A review (2018)The human factor in supply chain forecasting: A systematic review (2018)From Data to Causes II: Comparing Approaches to Panel Data Analysis (2019)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Forecasting with artificial neural networks: (1998)25 years of time series forecasting (2006)Variable Selection in Time Series Forecasting Using Random Forests (2017)Criteria for classifying forecasting methods (2019)Daily retail demand forecasting using machine learning with emphasis on calendric special days (2020)Forecasting methods and applications (1993)Forecasting: Methods and Applications (1999)How (In)accurate Are Demand Forecasts in Public Works Projects?: The Case of Transportation (2005)A review of demand forecasting models and methodological developments within tourism and passenger transportation industry (2019)Transportation demand analysis (1983)Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework (2009)The gravity model and trade flows: Recent developments in econometric modeling and empirical evidence (2017)Forecasting temporal world recovery in air transport markets in the presence of large economic shocks: The case of COVID-19 (2020)Some New Three Level Designs for the Study of Quantitative Variables (1960)Active travel for active ageing in China: The role of built environment (2019)Relating Environmental, Social, and Governance scores and sustainability performances of firms: An empirical analysis (2019)Examining non-linear built environment effects on elderly’s walking: A random forest approach (2020)Application of support vector machines in financial time series forecasting (2001)Analysis of the impact of high-speed railroad service on air transport demand (2005)Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology (2017)Time Series Analysis: Forecasting and Control (1977)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Testing the null hypothesis of stationarity against the alternative of a unit root (1992)Tourism demand forecasting using novel hybrid system (2013)New developments in tourism and hotel demand modeling and forecasting (2017)Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach (2018)Can we fly less? 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References

  • 2012Predicting the Present with Google Trends2,353 cited
  • 2002Comparing Predictive Accuracy1,745 cited
  • 2002Model-Dependent Variance Inflation Factor Cutoff Values1,209 cited
  • 2013Machine Learning Strategies for Time Series Forecasting568 cited
  • 2007Gravity models for airline passenger volume estimation297 cited
  • 2020Risks, resilience, and pathways to sustainable aviation: A COVID-19 perspective190 cited
  • 2013Forecasting of Hong Kong airport's passenger throughput171 cited
  • 2015Golden rule of forecasting: Be conservative169 cited
  • 2014A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting97 cited
  • 2014Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches96 cited
  • 2016Forecasting short-term air passenger demand using big data from search engine queries88 cited
  • 2019Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning84 cited
  • 2019Forecasting air passenger demand with a new hybrid ensemble approach84 cited
  • 2008A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application73 cited

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2021
Deep learning models for forecasting aviation demand time series
68 cited
  • 2019Nonlinear vector auto-regression neural network for forecasting air passenger flow64 cited
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