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Hierarchical forecasts for Australian domestic tourism
14 references·16 citing·39 second hop
Hierarchical forecasts for Australian domestic tourism (2008)Hierarchical forecasts for Australian dom…Optimal combination forecasts for hierarchical time series (2011)Optimal combination forecasts…Forecasting with Exponential Smoothing : The State Space Approach (2008)Forecasting with Exponential …Tourism demand modelling and forecasting—A review of recent research (2007)Tourism demand modelling and …Recent Developments in Econometric Modeling and Forecasting (2005)Recent Developments in Econom…Exponential smoothing with a damped multiplicative trend (2003)Exponential smoothing with a …A state space framework for automatic forecasting using exponential smoothing methods (2002)A state space framework for a…The M3-Competition: results, conclusions and implications (2000)The M3-Competition: results, …Comparing Predictive Accuracy (1995)Comparing Predictive AccuracyComparing Predictive Accuracy (1994)Comparing Predictive AccuracyExponential smoothing: The state of the art (1985)Exponential smoothing: The st…Production and inventory management (1983)Production and inventory mana…Is Aggregation Necessarily Bad? (1960)Is Aggregation Necessarily Ba…Production planning and inventory control (1958)Production planning and inven…Linear Aggregation of Economic Relations. (1956)Linear Aggregation of Economi…Forecast reconciliation: A review (2023)Forecast reconciliation: A re…Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team (2021)Visitor arrivals forecasts am…Understanding forecast reconciliation (2021)Understanding forecast reconc…A machine learning approach for forecasting hierarchical time series (2021)A machine learning approach f…Forecast reconciliation: A geometric view with new insights on bias correction (2020)Forecast reconciliation: A ge…Automatic robust estimation for exponential smoothing: Perspectives from statistics and machine learning (2020)Automatic robust estimation f…A cross-temporal hierarchical framework and deep learning for supply chain forecasting (2020)A cross-temporal hierarchical…Temporal hierarchies with autocorrelation for load forecasting (2019)Temporal hierarchies with aut…Cross-temporal coherent forecasts for Australian tourism (2019)Cross-temporal coherent forec…Tourism forecasting: A review of methodological developments over the last decade (2018)Tourism forecasting: A review…Fast computation of reconciled forecasts for hierarchical and grouped time series (2015)Fast computation of reconcile…‘Horses for Courses’ in demand forecasting (2014)‘Horses for Courses’ in deman…Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework (2013)Forecasting aggregate demand:…Forecasting Aggregate Demand: Analytical Comparison of Top-Down and Bottom-Up Approaches in a Multivariate Exponential Smoothing Framework (2013)Forecasting Aggregate Demand:…Combination of long term and short term forecasts, with application to tourism demand forecasting (2010)Combination of long term and …Modelling and forecasting daily international mass tourism to Peru (2009)Modelling and forecasting dai…Time Series Analysis, Forecasting and Control. (1971)Time Series Analysis: Forecasting and Control (1977)Forecasting Economic Time Series (2000)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)Combining forecasts: A review and annotated bibliography (1989)25 years of time series forecasting (2006)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Automatic Time Series Forecasting: The forecast Package for R (2008)Supply chain forecasting: Theory, practice, their gap and the future (2015)Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization (2018)Retail forecasting: Research and practice (2019)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)Statistical analysis of cointegration vectors (1988)Tourism supply chain management: A new research agenda (2009)Tourism economics research: A review and assessment (2012)A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting (2019)American Economic Review (2019)Forecasting Sales by Exponentially Weighted Moving Averages (1960)Time Series Analysis: Forecasting and Control (1977)Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Forecasting methods and applications (1993)A new metric of absolute percentage error for intermittent demand forecasts (2016)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts (1998)A Comprehensive Look at The Empirical Performance of Equity Premium Prediction (2007)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Economic Production Cycles with Imperfect Production Processes (1986)The Distribution Free Newsboy Problem: Review and Extensions (1993)Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information (2000)Testing the equality of prediction mean squared errors (1997)Consumption, Aggregate Wealth, and Expected Stock Returns (2001)Deep learning with long short-term memory networks for financial market predictions (2017)A note on the validity of cross-validation for evaluating autoregressive time series prediction (2017)A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2019)An optimized model using LSTM network for demand forecasting (2020)
this work references cited by second hop

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Cited by

  • 2014‘Horses for Courses’ in demand forecasting201 cited
  • 2018Tourism forecasting: A review of methodological developments over the last decade

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161 cited
  • 2015Fast computation of reconciled forecasts for hierarchical and grouped time series155 cited
  • 2010Combination of long term and short term forecasts, with application to tourism demand forecasting154 cited
  • 2013Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework143 cited
  • 2013Forecasting Aggregate Demand: Analytical Comparison of Top-Down and Bottom-Up Approaches in a Multivariate Exponential Smoothing Framework104 cited
  • 2020Forecast reconciliation: A geometric view with new insights on bias correction98 cited
  • 2019Cross-temporal coherent forecasts for Australian tourism93 cited
  • 2019Temporal hierarchies with autocorrelation for load forecasting86 cited
  • 2023Forecast reconciliation: A review85 cited
  • 2021Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team79 cited
  • 2020A cross-temporal hierarchical framework and deep learning for supply chain forecasting76 cited
  • 2021A machine learning approach for forecasting hierarchical time series74 cited
  • 2021Understanding forecast reconciliation70 cited
  • 2020Automatic robust estimation for exponential smoothing: Perspectives from statistics and machine learning64 cited
  • 2009Modelling and forecasting daily international mass tourism to Peru63 cited