Metraipolis › archive › dc_ca67f8fa102b

Matching of signal, noise and hardware timescales for filtering and forecasting of correlated noise signals — arXiv:2610.10037

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.10037
License (as archived): CC0-1.0
Archived: 2026-10-08T12:36:02.122Z
SHA-256 of the archived text: 8d22806dddaf96db92373f6c6efc7cb8285d647f036761d8c308ba9ec8e926b3
Stable URL of this page: https://metraipolis.com/documento/dc_ca67f8fa102b

How to cite

Matching of signal, noise and hardware timescales for filtering and forecasting of correlated noise signals — arXiv:2610.10037 — arXiv (en), https://arxiv.org/abs/2610.10037 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ca67f8fa102b (Metraipolis archive dc_ca67f8fa102b, sha256 8d22806dddaf96db92373f6c6efc7cb8285d647f036761d8c308ba9ec8e926b3)

BibTeX

@misc{dcca67f8fa102b,
  title = {Matching of signal, noise and hardware timescales for filtering and forecasting of correlated noise signals — arXiv:2610.10037},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.10037},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ca67f8fa102b; sha256 8d22806dddaf96db92373f6c6efc7cb8285d647f036761d8c308ba9ec8e926b3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.10037. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Physical reservoir computing exploits the nonlinear dynamics of physical systems to process time-dependent data with greater energy efficiency than conventional machine learning approaches. However, physical reservoirs have fixed intrinsic response timescales, whereas real-world signals combine deterministic and stochastic components across multiple timescales. Here we show, using a nanoporous niobium oxide reservoir, synthetic noisy signals and cryptocurrency-price volatility, that the relationship among noise correlation time, reservoir memory and forecast horizon determines whether correlated noise is filtered or predicted. Noise varying faster than the relevant reservoir memory and forecast horizon is averaged by the reservoir, whereas the temporal structure of slower-varying noise is sufficient for algorithmic forecasting. We introduce the reservoir memory horizon and forecasting regime index to distinguish these operating regimes. These contributions demonstrate that timescale matching can guide the encoding of input time series and development of physical reservoir architectures that filter, analyse and predict stochastic signal components across distinct temporal scales.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Disordered Systems and Neural Networks (cond-mat.dis-nn); Applied Physics (physics.app-ph); Data Analysis, Statistics and Probability (physics.data-an).
Pagina del lavoro: https://arxiv.org/abs/2610.10037 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T12:14:15.298Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).