On the categorization of demand patterns

By AA Syntetos, J. Boylan, J. Croston. 2005

433 citations recorded by the source corpus.

Subfields: Discrete Mathematics and Combinatorics, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019, background): “hourly to yearly. Table 1 in the supplementary Section S1 outlines its composition and summary statistics, showing wide variability in TS characteristics (smooth vs erratic) deﬁned by Syntetos et al. [28]. 2 )&6W DFN O D\HUV )& )& %DFNFDVW )RUHFDVW %ORFN,QSXW %ORFN %ORFN %ORFN¬ . 6WD”
Effective Bayesian Modeling of Groups of Related Count Time Series (2014, background): “The categorization of demand patterns into “smooth” (high non-zero demand rate, low CV(2)), “erratic” (both high), “intermittent” (both low) and “lumpy” (low demand rate, high CV(2)) follows Syntetos et al. (2005). The chosen datasets cover a broad mix of patterns.”
ABC Classification: Service Levels and Inventory Costs (2009, background): “We remark that forecasting procedures can also be class-dependent (Boylan et al. 2008; Syntetos et al. 2005).”
EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II (2006, positive): “Syntetos et al. (2005) extended Johnston and Boylan’s work by developing rules based on the variability of order size and inter-arrival times for selecting from three methods: N-N, original Croston, and modified Croston in Eqs.”
On the demand distributions of spare parts (2012, methodology): “Syntetos et al. (2005) took this work forward by developing a demand classification scheme that relies upon both p and the squared coefficient of variation of demand sizes (CV 2).”
Demand categorisation in a European spare parts logistics network (2009, background): “From a forecasting perspective, Syntetos et al. (2005)”
'Horses for Courses' in demand forecasting (2014, methodology): “We also considered the case of intermittent demand data, where two main factors were considered, namely average inter-demand interval (IDI) and squared coefficient of variation (CV 2 ) of the non-zero demands as it is dictated by the work of Syntetos et al. (2005).”
The inventory performance of forecasting methods: Evidence from the M3 competition data (2019, background): “(2008) extended this work to the case of classifying stock keeping units (SKUs) based on their demand characteristics (Syntetos et al., 2005).”
Intermittent demand forecasts with neural networks (2013, background): “Lumpy demand time series exhibit high variability of the non-zero demand (Syntetos et al., 2005).”
Forecasting and operational research: a review (2008, methodology): “This led researchers to examine the conditions under which Croston’s method performs better than SES, based on a classification scheme for the demand data (Syntetos et al, 2005; Boylan et al, 2006).”