Stochastically Structured Reservoir Computers for Financial and Economic System Identification
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914163361579008 |
|---|---|
| author | Banegas, Lendy Vides, Fredy |
| author_facet | Banegas, Lendy Vides, Fredy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17115 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stochastically Structured Reservoir Computers for Financial and Economic System Identification Banegas, Lendy Vides, Fredy Optimization and Control Systems and Control Theoretical Economics 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. |
| title | Stochastically Structured Reservoir Computers for Financial and Economic System Identification |
| topic | Optimization and Control Systems and Control Theoretical Economics |
| url | https://arxiv.org/abs/2507.17115 |