On the identification of sales forecasting models in the presence of promotions

By Juan Ramón Trapero, Nikolaos Kourentzes, Robert Fildes. 2015

106 citations recorded by the source corpus.

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

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Fotios Petropoulos
2. Rob J Hyndman
3. Konstantinos Nikolopoulos
4. Nikolaos Kourentzes
5. Vassilios Assimakopoulos
6. Spyros Makridakis
7. Evangelos Spiliotis
8. Christoph Bergmeir

In Statistics and Probability

1. Konstantinos Nikolopoulos
2. Vassilios Assimakopoulos
3. Nikolaos Kourentzes
4. Fotios Petropoulos
5. Evangelos Spiliotis
6. Spyros Makridakis
7. Robert Fildes
8. Christoph Bergmeir

In Time-Series & Forecasting

1. Fotios Petropoulos
2. Konstantinos Nikolopoulos
3. Rob J Hyndman
4. Spyros Makridakis
5. Nikolaos Kourentzes
6. Vassilios Assimakopoulos
7. Evangelos Spiliotis
8. Christoph Bergmeir

Citations in the corpus

• Retail forecasting: Research and practice (2019, background): “One alternative approach, yet to be explored in retail, is the use of time series aggregation through MAPA (Kourentzes, Petropoulos, and Trapero, 2014) to overcome the intermittence, which then could be translated into distribution centre loading.”
• Forecasting: theory and practice (2020, methodology): “forecast using traditional approaches since overfitting may easily occur or models cannot even be estimated (Trapero et al., 2015).”
• Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions (2019, methodology): “Trapero et al. (2015) use a similar idea in their work for demand forecasting of stock-keeping units (SKUs).”