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On the categorization of demand patterns
1 references·16 citing·46 second hop
On the categorization of demand patterns (2004)On the categorization of demand patternsForecasting and Stock Control for Intermittent Demands (1972)Forecasting and Stock Control…The M5 competition: Background, organization, and implementation (2021)The M5 competition: Backgroun…Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion (2020)Demand forecasting in supply …Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor (2020)Deep reinforcement learning f…Retail forecasting: Research and practice (2019)Retail forecasting: Research …OR in spare parts management: A review (2017)OR in spare parts management:…Single-hidden layer neural networks for forecasting intermittent demand (2016)Single-hidden layer neural ne…Evaluating predictive count data distributions in retail sales forecasting (2016)Evaluating predictive count d…Supply chain forecasting: Theory, practice, their gap and the future (2015)Supply chain forecasting: The…‘Horses for Courses’ in demand forecasting (2014)‘Horses for Courses’ in deman…Intermittent demand forecasts with neural networks (2013)Intermittent demand forecasts…Intermittent demand: Linking forecasting to inventory obsolescence (2011)Intermittent demand: Linking …An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis (2010)An aggregate–disaggregate int…ABC Classification: Service Levels and Inventory Costs (2009)ABC Classification: Service L…Forecasting and operational research: a review (2008)Forecasting and operational r…Lumpy demand forecasting using neural networks (2007)Lumpy demand forecasting usin…Classification for forecasting and stock control: a case study (2006)Classification for forecastin…Time Series Analysis: Forecasting and Control (1977)25 years of time series forecasting (2006)Automatic Time Series Forecasting: The forecast Package for R (2008)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Error measures for generalizing about forecasting methods: Empirical comparisons (1992)Forecasting for inventory planning: a 50-year review (2009)UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution (2019)On the psychology of prediction. 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References

  • 1972Forecasting and Stock Control for Intermittent Demands761 cited

Cited by

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  • 2021The M5 competition: Background, organization, and implementation

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143 cited
  • 2020Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor140 cited
  • 2016Single-hidden layer neural networks for forecasting intermittent demand140 cited