A model-free diffusion test for discrete time series that uses the scaling of excursion counts with quadratic variation to classify signals as stochastic or deterministic.
Mathematical Analysis of Random Noise
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The t^{-3/2} decay of shed vorticity in 2D inviscid flow makes the second moment diverge logarithmically, so finite-dimensional system-ID models fitted to such data parameterize the observation horizon rather than intrinsic physics.
citing papers explorer
-
Detecting Stochasticity in Discrete Signals via Nonparametric Excursion Theorem
A model-free diffusion test for discrete time series that uses the scaling of excursion counts with quadratic variation to classify signals as stochastic or deterministic.
-
The inviscid Euler limit as a critical boundary for moment-based aerodynamic system identification
The t^{-3/2} decay of shed vorticity in 2D inviscid flow makes the second moment diverge logarithmically, so finite-dimensional system-ID models fitted to such data parameterize the observation horizon rather than intrinsic physics.