Model combinations through revised base-rates

Fotios Petropoulos, Evangelos Spiliotis, Anastasios Panagiotelis

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)
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Standard selection criteria for forecasting models focus on information that is calculated for each series independently, disregarding the general tendencies and performance of the candidate models. In this paper, we propose a new way to perform statistical model selection and model combination that incorporates the base rates of the candidate forecasting models, which are then revised so that the per-series information is taken into account. We examine two schemes that are based on the precision and sensitivity information from the contingency table of the base rates. We apply our approach on pools of either exponential smoothing or ARMA models, considering both simulated and real time series, and show that our schemes work better than standard statistical benchmarks. We test the significance and sensitivity of our results, discuss the connection of our approach to other cross-learning approaches, and offer insights regarding implications for theory and practice.

Original languageEnglish
Pages (from-to)1477-1492
JournalInternational Journal of Forecasting
Issue number3
Early online date24 Aug 2022
Publication statusPublished - 31 Jul 2023

Bibliographical note

No funding was acknowledged.


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