The paper investigates how the intrinsic response timescales of a physical reservoir computing system—specifically a nanoporous niobium oxide reservoir—interact with the correlation times of noisy signals and the desired forecast horizon. By analyzing synthetic noisy data and cryptocurrency price volatility, the authors demonstrate that noise components faster than the reservoir’s memory and forecast horizon are effectively averaged out, while slower-varying noise can be algorithmically forecasted. They introduce the concepts of reservoir memory horizon and forecasting regime index to delineate these operating regimes and suggest that matching timescales can guide the design of reservoirs for filtering, analyzing, and predicting stochastic signals across different temporal scales.
arXiv:2607. 24420v1 Announce Type: cross Abstract: Reservoir computing has emerged as an efficient machine learning framework for predicting time series generated by dynamical systems.
arXiv:2611.06051v1 Announce Type: cross Abstract: Reservoir computing provides an efficient framework for time-series prediction because only the readout layer is trained while the recurrent reservoi...
arXiv:2510. 13634v2 Announce Type: replace Abstract: Quantum reservoir computing (QRC) offers a hardware-friendly approach to temporal learning, yet most studies target univariate signals and overlook near-term hardware constraints.
arXiv:2504. 17503v2 Announce Type: replace Abstract: We study how the degree of nonlinearity in the input data affects the optimal design of reservoir computers, focusing on how closely the model's nonlinearity should align with that of the data.
arXiv:2607. 23288v1 Announce Type: cross Abstract: Photonic reservoir computing is a promising physical machine-learning technique for predicting time-series data.
arXiv:2609.24754v1 Announce Type: new Abstract: We investigate next generation reservoir computing (NGRC) as a data-driven approach for inferring unseen components of dynamical systems. We compare NG...
arXiv:2509. 24122v3 Announce Type: replace Abstract: At the heart of time-series forecasting (TSF) lies a fundamental challenge: how can models efficiently and effectively capture long-range temporal dependencies across ever-growing sequences?
arXiv:2607. 11272v1 Announce Type: cross Abstract: Accurate dengue forecasting is crucial for public health planning, but remains challenging because incidence series are often short, noisy, non-stationary, nonlinear, and often affected by long-range temporal dependence.
MoFE is a deep learning framework that combines Fourier Neural Operators with a Mixture-of-Experts architecture to forecast cryptocurrency prices. It models volatility as a mix of multi-frequency components—including fundamental growth, mining costs, halving events, and market sentiment—using adaptive FNO and convolutional experts. Experiments on Bitcoin data from 2020 to 2025 show MoFE outperforms existing models in short‑term horizons, reducing phase‑lag errors and improving directional accuracy and information coefficient, which translates into higher Sharpe ratios in simulated trading.
arXiv:2609. 11071v1 Announce Type: cross Abstract: Quantum reservoir computing (QRC) uses quantum dynamics to represent input histories for prediction through a trained classical readout.
arXiv:2609.32384v2 Announce Type: replace Abstract: Real-world time series are inherently non-stationary, with trends, periodic patterns, and uncertainty evolving over time. While the Fourier domain...
arXiv:2607. 17858v1 Announce Type: cross Abstract: Reservoir computing exploits nonlinear dynamical systems to encode temporal inputs into high-dimensional state space representations.