Forecasting the intermittent demand for slow-moving inventories: A modelling approach

By Ralph D. Snyder, J. Keith Ord, A. Beaumont. 2012

126 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research, Statistics and Probability, Time-Series & Forecasting

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Citations in the corpus

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks (2017, methodology): “For prediction, the history of the time series z i,t is fed in for t t 0 , then in the prediction range (right) for t ≥ t 0 a sample ˆ z i,t ∼ (cid:96) ( ·| θ i,t ) is drawn and fed back for the next point until the end of the prediction range t = t 0 + T generating one sample trace.”
Effective Bayesian Modeling of Groups of Related Count Time Series (2014, methodology): “…of the proposed H-NBSS model against the following benchmarks: (i) Croston’s (1972) method, (ii) simple exponential smoothing (E-S) with additive errors and an automaticallyadjusted smoothing constant, and (iii) the damped dynamic model with negative binomial observations of Snyder et al. (2012).”
Forecasting: theory and practice (2020, methodology): “Since then several advanced time series methods have been developed and evaluated (Taylor, 2010; De Livera et al., 2011; Taylor and Snyder, 2012), including artificial neural networks (Millán-Ruiz and Hidalgo, 2013; Pacheco et al., 2009; Li et al., 2011) and models for density forecasting (Taylor,…”
Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping ☆ (2015, methodology): “Alternative bootstrapping methods are found in Efron (1979), Snyder (2002), Willemain, Smart, & Schwarz (2004, hereafter WSS), Porras and Dekker (2008), Teunter and Duncan (2009), Zhou and Viswanathan (2011), and Snyder et al. (2012).”
Probabilistic Forecasting with Temporal Convolutional Neural Network (2019, methodology): “For example, we can choose negative-binomial distribution for long-tail products, which is traditionally used for modeling over-dispersed count data and has been shown to perform well in empirical studies (Villani et al., 2012; Snyder et al., 2012; Syntetos et al., 2015; Salinas et al., 2019).”
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale (2017, methodology): “Previous work on intermittent demand forecasting in Statistics is surveyed in [18]: none of these address longer lead times.”
Bayesian Intermittent Demand Forecasting for Large Inventories (2016, methodology): “The best-performing method in [15] uses negative binomial likelihood and a damped dynamic, parameters are learned by maximum likelihood.”
On the identification of sales forecasting models in the presence of promotions (2015, background): “Particularly, recent studies (Davydenko and Fildes, 2013; Snyder et al., 2012) propose the Average Relative MAE and distribution based scores respectively, to enhance the forecasting accuracy measure in supply chain forecasting applications.”