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On the bias of intermittent demand estimates
1 references·16 citing·33 second hop
On the bias of intermittent demand estimates (2001)On the bias of intermittent demand estima…Forecasting and Stock Control for Intermittent Demands (1972)Forecasting and Stock Control…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…Single-hidden layer neural networks for forecasting intermittent demand (2016)Single-hidden layer neural ne…Forecast combinations for intermittent demand (2014)Forecast combinations for int…‘Horses for Courses’ in demand forecasting (2014)‘Horses for Courses’ in deman…Intermittent demand forecasts with neural networks (2013)Intermittent demand forecasts…Demand Forecasting in the Fashion Industry: A Review (2013)Demand Forecasting in the Fas…Intermittent demand: Linking forecasting to inventory obsolescence (2011)Intermittent demand: Linking …Forecasting the intermittent demand for slow-moving inventories: A modelling approach (2011)Forecasting the intermittent …An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis (2010)An aggregate–disaggregate int…Forecasting for inventory planning: a 50-year review (2009)Forecasting for inventory pla…Forecasting intermittent demand: a comparative study (2008)Forecasting intermittent dema…Forecasting and operational research: a review (2008)Forecasting and operational r…Lumpy demand forecasting using neural networks (2007)Lumpy demand forecasting usin…On the stock control performance of intermittent demand estimators (2005)On the stock control performa…Forecasting for the ordering and stock-holding of spare parts (2004)Forecasting for the ordering …Inventory control with a modified Croston procedure and Erlang distribution (2003)Inventory control with a modi…Forecasting: Methods and Applications (1984)Forecasting methods and applications (1993)Evaluating predictive count data distributions in retail sales forecasting (2016)The theta model: a decomposition approach to forecasting (2000)Another look at measures of forecast accuracy (2006)The human factor in supply chain forecasting: A systematic review (2018)UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution (2019)Stock Control with Sporadic and Slow-Moving Demand (1984)Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method (1994)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)Inventory management and production planning and scheduling (1999)The accuracy of intermittent demand estimates (2004)Judging the judges through accuracy-implication metrics: The case of inventory forecasting (2009)Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping (2015)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Error measures for generalizing about forecasting methods: Empirical comparisons (1992)On the psychology of prediction. (1973)Thinking, Fast and Slow (2011)Exploring the sources of uncertainty: Why does bagging for time series forecasting work? (2018)Judgmental selection of forecasting models (2018)Daily retail demand forecasting using machine learning with emphasis on calendric special days (2020)Forecasting with artificial neural networks: (1998)Out-of-sample tests of forecasting accuracy: an analysis and review (2000)An overview of recent literature on spare parts inventories (2002)An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain (2019)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction (2011)Variable Selection in Time Series Forecasting Using Random Forests (2017)
this work references cited by second hop

References

  • 1972Forecasting and Stock Control for Intermittent Demands761 cited

Cited by

  • 2008Forecasting and operational research: a review287 cited
  • 2011Intermittent demand: Linking forecasting to inventory obsolescence255 cited
  • 2013Intermittent demand forecasts with neural networks231 cited
  • 2007Lumpy demand forecasting using neural networks220 cited
  • 2004Forecasting for the ordering and stock-holding of spare parts214 cited
  • 2014‘Horses for Courses’ in demand forecasting201 cited
  • 2008Forecasting intermittent demand: a comparative study183 cited
  • 2010An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis154 cited
  • 2005On the stock control performance of intermittent demand estimators149 cited
  • 2009Forecasting for inventory planning: a 50-year review146 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
  • 2011Forecasting the intermittent demand for slow-moving inventories: A modelling approach133 cited

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2013
Demand Forecasting in the Fashion Industry: A Review
131 cited
  • 2003Inventory control with a modified Croston procedure and Erlang distribution129 cited
  • 2014Forecast combinations for intermittent demand129 cited