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Combining probabilistic forecasts of intermittent demand
16 references·0 citing·35 second hop
Combining probabilistic forecasts of intermittent demand (2024)Combining probabilistic forecasts of inte…M5 accuracy competition: Results, findings, and conclusions (2022)M5 accuracy competition: Resu…Forecasting: theory and practice (2022)Forecasting: theory and pract…Forecast combinations: An over 50-year review (2022)Forecast combinations: An ove…Retail forecasting: Research and practice (2019)Retail forecasting: Research …DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)DeepAR: Probabilistic forecas…Probabilistic Forecasting (2014)Probabilistic ForecastingOptimal prediction pools (2011)Optimal prediction poolsAutomatic Time Series Forecasting: The forecast Package for R (2008)Automatic Time Series Forecas…Combining density forecasts (2007)Combining density forecastsThe accuracy of intermittent demand estimates (2004)The accuracy of intermittent …On the categorization of demand patterns (2004)On the categorization of dema…Combination forecasts of output growth in a seven‐country data set (2004)Combination forecasts of outp…A new approach to forecasting intermittent demand for service parts inventories (2003)A new approach to forecasting…A state space framework for automatic forecasting using exponential smoothing methods (2002)A state space framework for a…A quantile regression neural network approach to estimating the conditional density of multiperiod returns (2000)A quantile regression neural …Forecasting and Stock Control for Intermittent Demands (1972)Forecasting and Stock Control…Time Series Analysis: Forecasting and Control (1977)25 years of time series forecasting (2006)The Combination of Forecasts (1969)Forecasting Using Principal Components From a Large Number of Predictors (2002)Macroeconomic Forecasting Using Diffusion Indexes (2002)Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2009)Forecasting Stock Returns (2013)Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2016)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)The M3-Competition: results, conclusions and implications (2000)A Survey of Forecast Error Measures (2013)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)Another look at measures of forecast accuracy (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)Forecasting methods and applications (1993)Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011)A new metric of absolute percentage error for intermittent demand forecasts (2016)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)Forecasting with artificial neural networks: (1998)Designing and managing the supply chain : concepts, strategies, and case studies (2000)N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019)Fuzzy sets (1965)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)User Acceptance of Computer Technology: A Comparison of Two Theoretical Models (1989)Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2022)Forecast evaluation for data scientists: common pitfalls and best practices (2022)Comparing Predictive Accuracy (1995)Coherent Measures of Risk (1999)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Evaluating Probabilistic Forecasts with scoringRules (2019)Forecasting and operational research: a review (2008)Supply chain forecasting: Theory, practice, their gap and the future (2015)The M5 competition: Background, organization, and implementation (2021)
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References

  • 2008Automatic Time Series Forecasting: The forecast Package for R3,538 cited
  • 2019DeepAR: Probabilistic forecasting with autoregressive recurrent networks2,726 cited
  • 2004Combination forecasts of output growth in a seven‐country data set1,138 cited
  • 2002A state space framework for automatic forecasting using exponential smoothing methods1,074 cited
  • 2022Forecasting: theory and practice942 cited
  • 2014Probabilistic Forecasting901 cited
  • 1972Forecasting and Stock Control for Intermittent Demands761 cited
  • 2022M5 accuracy competition: Results, findings, and conclusions579 cited
  • 2004The accuracy of intermittent demand estimates498 cited
  • 2004On the categorization of demand patterns432 cited
  • 2003A new approach to forecasting intermittent demand for service parts inventories395 cited
  • 2000A quantile regression neural network approach to estimating the conditional density of multiperiod returns387 cited
  • 2011Optimal prediction pools369 cited
  • 2019Retail forecasting: Research and practice357 cited
  • 2007Combining density forecasts309 cited
  • 2022

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Forecast combinations: An over 50-year review
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