GROEC: Combination method via Generalized Rolling Origin Evaluation
By J. A. Fiorucci, Francisco Louzada. 2020
20 citations recorded by the source corpus.
Abstract Combination methods have performed well in time series forecast competitions. This study proposes a simple but general methodology for combining time series forecast methods. Weights are calculated using a cross-validation scheme that assigns greater weights to methods with more accurate in-sample predictions. The methodology was used to combine forecasts from the Theta, exponential smoothing, and ARIMA models, and placed fifth in the M4 Competition for both point and interval forecasting.
Subfields: Management Science and Operations Research, Statistics and Probability
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In Management Science and Operations Research
- Fotios Petropoulos
- Spyros Makridakis
- Rob J Hyndman
- Konstantinos Nikolopoulos
- Vassilios Assimakopoulos
- Evangelos Spiliotis
- Sven F. Crone
- Nikolaos Kourentzes
In Statistics and Probability
Citations in the corpus
- The M4 competition: Bigger. Stronger. Better (2020, methodology): “Pawlikowski et al. (2019) and Jaganathan and Prakash (2019) suggest the use of different pools of models per frequency, while Fiorucci and Louzada (2019) combine the forecasts from three models: Theta, exponential smoothing and ARIMA.”