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On the empirical performance of some new neural network methods for forecasting intermittent demand
16 references·2 citing·31 second hop
On the empirical performance of some new neural network methods for forecasting intermittent demand (2020)On the empirical performance of some new …Supply chain forecasting: Theory, practice, their gap and the future (2015)Supply chain forecasting: The…Intermittent demand forecasts with neural networks (2013)Intermittent demand forecasts…Intermittent demand: Linking forecasting to inventory obsolescence (2011)Intermittent demand: Linking …Lumpy demand forecasting using neural networks (2007)Lumpy demand forecasting usin…An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods (2007)An inventory control system f…Exponential smoothing: The state of the art—Part II (2006)Exponential smoothing: The st…Another look at measures of forecast accuracy (2006)Another look at measures of f…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 …A new approach to forecasting intermittent demand for service parts inventories (2003)A new approach to forecasting…On the bias of intermittent demand estimates (2001)On the bias of intermittent d…Forecasting with artificial neural networks: (1998)Forecasting with artificial n…Forecasting for Items with Intermittent Demand (1996)Forecasting for Items with In…Forecasting and Stock Control for Intermittent Demands (1972)Forecasting and Stock Control…Statistical Forecasting for Inventory Control. (1960)Statistical Forecasting for I…On the use of machine learning in supply chain management: a systematic review (2023)On the use of machine learnin…Basket data-driven approach for omnichannel demand forecasting (2022)Basket data-driven approach f…Time Series Analysis: Forecasting and Control (1977)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Financial time series forecasting using support vector machines (2003)Time series forecasting using a hybrid ARIMA and neural network model (2003)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)Production and inventory management (1983)Inventory management and production planning and scheduling (1999)Forecasting Sales by Exponentially Weighted Moving Averages (1960)The M3-Competition: results, conclusions and implications (2000)Forecasting seasonals and trends by exponentially weighted moving averages (2004)A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2019)Artificial intelligence applications in supply chain management (2021)Forecasting: theory and practice (2022)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)A Survey of Forecast Error Measures (2013)Information Distortion in a Supply Chain: The Bullwhip Effect (1997)Supply Chain Management: Strategy, Planning and Operations (2000)Supply Chain Management: Strategy, Planning, and Operation (2003)Literature review: Strategic network optimization models in waste reverse supply chains (2018)Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion (2020)Machine learning demand forecasting and supply chain performance (2020)The Savings Investment and Valuation of a Corporation (1962)Experience with Forecasting Univariate Time Series and the Combination of Forecasts (1974)ARMA Models and the Box-Jenkins Methodology (1997)Forecasting and operational research: a review (2008)Retail forecasting: Research and practice (2019)25 years of time series forecasting (2006)Automatic Time Series Forecasting: The forecast Package for R (2008)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)
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  • 2007An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods

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188 cited

Cited by

  • 2022Basket data-driven approach for omnichannel demand forecasting39 cited
  • 2023On the use of machine learning in supply chain management: a systematic review37 cited