.png){kind=icon}
.png){kind=icon}
Return
GROEC: Combination method via Generalized Rolling Origin Evaluation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijforecast.2019.04.013.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2019 Published by Elsevier B.V. on behalf of International Institute of Forecasters.
Keywords:
M4 competition
Forecast combination
Theta models
ARIMA models
Exponential smoothing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
3.1K
Citations:
9.9K