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The Accuracy of Non‐traditional versus Traditional Methods of Forecasting Lumpy Demand
16 references·2 citing·31 second hop
The Accuracy of Non‐traditional versus Traditional Methods of Forecasting Lumpy Demand (2011)The Accuracy of Non‐traditional versus Tr…Lumpy demand forecasting using neural networks (2007)Lumpy demand forecasting usin…Classification for forecasting and stock control: a case study (2006)Classification for forecastin…The accuracy of intermittent demand estimates (2004)The accuracy of intermittent …On the categorization of demand patterns (2004)On the categorization of dema…Forecasting for the ordering and stock-holding of spare parts (2004)Forecasting for the ordering …Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model (2003)Evaluation of forecasting met…On the bias of intermittent demand estimates (2001)On the bias of intermittent d…Neural network versus econometric models in forecasting inflation (2000)Neural network versus econome…Neural Network Models for Time Series Forecasts (1996)Neural Network Models for Tim…Forecasting for Items with Intermittent Demand (1996)Forecasting for Items with In…Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method (1994)Forecasting intermittent dema…The evaluation of extrapolative forecasting methods (1992)The evaluation of extrapolati…Error measures for generalizing about forecasting methods: Empirical comparisons (1992)Error measures for generalizi…The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)The accuracy of extrapolation…Note. Evaluation of extrapolative forecasting methods: Results of a survey of academicians and practitioners (1982)Note. Evaluation of extrapola…Forecasting and Stock Control for Intermittent Demands (1972)Forecasting and Stock Control…Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics (2021)Cross‐item learning for volat…On the empirical performance of some new neural network methods for forecasting intermittent demand (2020)On the empirical performance …Averages of Forecasts: Some Empirical Results (1983)Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data (1998)The use of data mining and neural networks for forecasting stock market returns (2005)Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm (2010)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)Forecasting Sales by Exponentially Weighted Moving Averages (1960)Forecasting methods and applications (1993)Forecasting seasonals and trends by exponentially weighted moving averages (2004)Forecasting intermittent demand: a comparative study (2008)Intermittent demand forecasts with neural networks (2013)Experience with Forecasting Univariate Time Series and the Combination of Forecasts (1974)Exponential smoothing: The state of the art (1985)The M2-competition: A real-time judgmentally based forecasting study (1993)The state of macroeconomic forecasting (2002)A new accuracy measure based on bounded relative error for time series forecasting (2017)Decision Systems for Inventory Management and Production Planning (1979)Production and inventory management (1983)The M3-Competition: results, conclusions and implications (2000)A Survey of Forecast Error Measures (2013)The Combination of Forecasts (1969)Combining forecasts: A review and annotated bibliography (1990)Forecasting and operational research: a review (2008)Supply chain forecasting: Theory, practice, their gap and the future (2015)Retail forecasting: Research and practice (2019)A state space framework for automatic forecasting using exponential smoothing methods (2002)Making and Evaluating Point Forecasts (2011)A new metric of absolute percentage error for intermittent demand forecasts (2016)Intermittent demand: Linking forecasting to inventory obsolescence (2011)
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References

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  • 1992Error measures for generalizing about forecasting methods: Empirical comparisons1,400 cited
  • 1972Forecasting and Stock Control for Intermittent Demands761 cited
  • 2004The accuracy of intermittent demand estimates498 cited
  • 1996Neural Network Models for Time Series Forecasts468 cited
  • 2004On the categorization of demand patterns432 cited
  • 2001On the bias of intermittent demand estimates363 cited
  • 1992The evaluation of extrapolative forecasting methods265 cited
  • 1994Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method236 cited
  • 2003Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model235 cited
  • 2007Lumpy demand forecasting using neural networks220 cited
  • 2004Forecasting for the ordering and stock-holding of spare parts214 cited
  • 1996Forecasting for Items with Intermittent Demand199 cited
  • 2006Classification for forecasting and stock control: a case study164 cited
  • 2000Neural network versus econometric models in forecasting inflation

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155 cited
  • 1982Note. Evaluation of extrapolative forecasting methods: Results of a survey of academicians and practitioners138 cited
  • Cited by

    • 2021Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics44 cited
    • 2020On the empirical performance of some new neural network methods for forecasting intermittent demand41 cited