Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping ☆

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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).”
Forecasting: theory and practice (2020, background): “Boylan and Syntetos (2003), Shale et al. (2006), and Syntetos et al. (2015a) presented correction factors to overcome the bias associated with Croston’s approach under a Poisson demand arrival process and/or estimation of demand sizes and intervals using an SMA.”
The inventory performance of forecasting methods: Evidence from the M3 competition data (2019, methodology): “…of parametric methods for forecasting intermittent demand has also been compared against bootstrapping methods (Teunter and Duncan, 2009; Syntetos et al., 2015) and neural method techniques (Kourentzes, 2013), with interesting insights emerging from the inventory versus realised service…”