FEG-Pro estimates finite-horizon forecast-error growth slopes from scalar time series via kNN multi-horizon forecasting as proxies for largest Lyapunov exponents, while extracting additional profile descriptors.
Kantz, A robust method to estimate the maximal lyapunov exponent of a time series, Physics Letters A 185 (1) (1994) 77–87
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
nlin.CD 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
FEG-Pro: Forecast-Error Growth Profiling for Finite-Horizon Instability Analysis of Nonlinear Time Series
FEG-Pro estimates finite-horizon forecast-error growth slopes from scalar time series via kNN multi-horizon forecasting as proxies for largest Lyapunov exponents, while extracting additional profile descriptors.