Some Theoretical Results on Forecast Combinations
By Felix Chan, Laurent L. Pauwels. 2018
78 citations recorded by the source corpus.
Subfields: Economics and Econometrics, 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 Economics and Econometrics
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Rob J Hyndman
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Evangelos Spiliotis
- Spyros Makridakis
- Christoph Bergmeir
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
- A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2020, unknown): “This process enhances the robustness of the method further, mitigating the model and parameter uncertainty (Petropoulos, Hyndman, & Bergmeir, 2018) while also exploiting the beneficial effects of combining (Chan & Pauwels, 2018).”
- Tales from tails: On the empirical distributions of forecasting errors and their implication to risk (2019, methodology): “Given that many studies claim benefits in risk reduction even when simple combinations are considered (Chan & Pauwels, 2018), this method displays great interest.”
- Forecasting: theory and practice (2020, background): “searchers have tried to understand why and when combinations improve forecast accuracy (Palm and Zellner, 1992; Petropoulos et al., 2018a; Timmermann, 2006), and the popularity of the simple average (Chan and Pauwels, 2018; Smith and Wallis, 2009a; Claeskens et al., 2016). Others have investigated p”