Evaluating predictive count data distributions in retail sales forecasting
By Stephan Kolassa. 2016
148 citations recorded by the source corpus.
Subfields: Finance, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
- David F. Hendry
- David E. Rapach
- Michael P. Clements
- Shaohui Ma
- Robert Fildes
- Nikolaos Kourentzes
- Jennifer L. Castle
- Feng Li
In Management Science and Operations Research
- Fotios Petropoulos
- Rob J Hyndman
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Evangelos Spiliotis
- Christoph Bergmeir
Citations in the corpus
- Forecasting: theory and practice (2020, background): “However, this is an under-investigated aspect of retail forecasting (Taylor, 2007; Kolassa, 2016).”
- Retail forecasting: Research and practice (2019, methodology): “…tested on demand/ sales time series data from industries other than retail (e.g., service/spare parts, high-priced capital goods in electronics, automotive, aerospace and high tech), except for Kolassa (2016), who assessed density forecasts based on Croston’s method and found them sorely lacking.”