Matching of signal, noise and hardware timescales for filtering and forecasting of correlated noise signals
Read the original on arXiv Machine Learning →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.
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