Model combinations through revised base-rates

TL;DR

This paper introduces a novel model selection and combination method that revises base-rates of forecasting models using per-series information, improving performance over standard benchmarks.

Contribution

It proposes a new statistical approach to model selection and combination that incorporates and revises base-rates based on per-series data, enhancing forecast accuracy.

Findings

Our schemes outperform standard benchmarks on real time series.

Revised base-rate methods improve model selection accuracy.

Approach connects to cross-learning techniques and offers practical insights.

Abstract

Standard selection criteria for forecasting models focus on information that is calculated for each series independently, disregarding the general tendencies and performances of the candidate models. In this paper, we propose a new way to 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 exponential smoothing models and a large number of real time series and we show that our schemes work better than standard statistical benchmarks. We discuss the connection of our approach to other cross-learning approaches and offer insights regarding implications for theory and practice.