{"ID":2891126,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17115","arxiv_id":"2507.17115","title":"Stochastically Structured Reservoir Computers for Financial and Economic System Identification","abstract":"This paper introduces a methodology for identifying and simulating financial and economic systems using stochastically structured reservoir computers (SSRCs). The framework combines structure-preserving embeddings with graph-informed coupling matrices to model inter-agent dynamics while enhancing interpretability. A constrained optimization scheme guarantees compliance with both stochastic and structural constraints. Two empirical case studies, a nonlinear stochastic dynamic model and regional inflation network dynamics, demonstrate the effectiveness of the approach in capturing complex nonlinear patterns and enabling interpretable predictive analysis under uncertainty.","short_abstract":"This paper introduces a methodology for identifying and simulating financial and economic systems using stochastically structured reservoir computers (SSRCs). The framework combines structure-preserving embeddings with graph-informed coupling matrices to model inter-agent dynamics while enhancing interpretability. A co...","url_abs":"https://arxiv.org/abs/2507.17115","url_pdf":"https://arxiv.org/pdf/2507.17115v3","authors":"[\"Lendy Banegas\",\"Fredy Vides\"]","published":"2025-07-23T01:35:33Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"econ.TH\",\"eess.SY\"]","methods":"[]","has_code":false}