TY - JOUR
T1 - Model combinations through revised base-rates
AU - Petropoulos, Fotios
AU - Spiliotis, Evangelos
AU - Panagiotelis, Anastasios
N1 - No funding was acknowledged.
PY - 2023/7/31
Y1 - 2023/7/31
N2 - 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.
AB - 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.
U2 - 10.1016/j.ijforecast.2022.07.010
DO - 10.1016/j.ijforecast.2022.07.010
M3 - Article
SN - 0169-2070
VL - 39
SP - 1477
EP - 1492
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 3
ER -