Treating and Pruning: new approaches to forecasting model selection and combination using prediction intervals

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 35
  • Download : 0
We propose a new way of selecting among model forms in automated exponential smoothing routines, consequently enhancing their predictive power. The procedure, here addressed as treating, operates by selectively subsetting the ensemble of competing models based on information from their prediction intervals. By the same token, we set forth a pruning strategy to improve the accuracy of both point forecasts and prediction intervals in forecast combination methods. The proposed approaches are respectively applied to automated exponential smoothing routines and Bagging algorithms, to demonstrate their potential. An empirical experiment is conducted on a wide range of series from the M-Competitions. The results attest that the proposed approaches are simple, without requiring much additional computational cost, but capable of substantially improving forecasting accuracy for both point forecasts and prediction intervals, outperforming important benchmarks and recently developed forecast combination methods. © 2020 International Institute of Forecasters
Publisher
ELSEVIER
Issue Date
2021-04
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF FORECASTING, v.37, no.2 , pp.547 - 568

ISSN
0169-2070
DOI
10.1016/j.ijforecast.2020.07.005
URI
http://hdl.handle.net/10203/281224
Appears in Collection
RIMS Journal Papers
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0