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.
Discrim- inating chaotic and stochastic time series using permutation entropy and artificial neural networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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.