{"ID":2829111,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.13642","arxiv_id":"2512.13642","title":"From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles","abstract":"Model combination is a powerful approach for achieving superior performance compared to selecting a single model. 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). The Hedge and Follow-the-Leader schemes are discussed, and their online learning guarantees are extended to settings with dependent data. In empirical applications, the proposed Ensemble Echo State Networks demonstrate significantly improved predictive performance relative to individual MFESN models.","short_abstract":"Model combination is a powerful approach for achieving superior performance compared to selecting a single model. 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 foreca...","url_abs":"https://arxiv.org/abs/2512.13642","url_pdf":"https://arxiv.org/pdf/2512.13642v2","authors":"[\"Giovanni Ballarin\",\"Lyudmila Grigoryeva\",\"Yui Ching Li\"]","published":"2025-12-15T18:36:58Z","proceeding":"econ.EM","tasks":"[\"econ.EM\",\"stat.ML\"]","methods":"[]","has_code":false}