Multi-horizon kNN forecast-error growth that fits Mittag–Leffler better than exponential is a preliminary diagnostic of fractional-memory dynamics in scalar time series.
A novel approach for estimating largest lyapunov exponents in one-dimensional chaotic time series using machine learning
2 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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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.
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Mittag-Leffler-Type Forecast-Error Growth as a Diagnostic Indicator of Fractional Dynamics
Multi-horizon kNN forecast-error growth that fits Mittag–Leffler better than exponential is a preliminary diagnostic of fractional-memory dynamics in scalar time series.
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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.