From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles
Start Date
2024
Description
Model combination is a powerful approach to achieve superior performance with
a set of models than by just selecting any single one. We study both theoretically
and empirically the effectiveness of ensembles of Multi-Frequency Echo State Networks
(MFESNs), which have been shown to achieve state-of-the-art macroeconomic time series
forecasting results (Ballarin et al., 2024a). Hedge and Follow-the-Leader schemes are
discussed, and their online learning guarantees are extended to the case of dependent
data. In applications, our proposed Ensemble Echo State Networks show significantly
improved predictive performance compared to individual MFESN models.
a set of models than by just selecting any single one. We study both theoretically
and empirically the effectiveness of ensembles of Multi-Frequency Echo State Networks
(MFESNs), which have been shown to achieve state-of-the-art macroeconomic time series
forecasting results (Ballarin et al., 2024a). Hedge and Follow-the-Leader schemes are
discussed, and their online learning guarantees are extended to the case of dependent
data. In applications, our proposed Ensemble Echo State Networks show significantly
improved predictive performance compared to individual MFESN models.
Leader contributor(s)
Member contributor(s)
Yui Ching Li
Funder