5 papers · 1 filter
Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling
Andrei Velichko
We investigate whether the past of an auxiliary time series provides predictive information about a target beyond that contained in the target's own history. We compare matched out…
Data-Driven Characterisation of Wave-Forced Turbulence Using Time-Resolved Forecast-Error Growth
Raj Jyoti Baishya, Joychen Kenglang, Andrei Velichko +1
Periodic surface-wave forcing can reorganise turbulent flows through coherent spectral response, synchronization, intermittency, and changes in short-term predictability, so its dy…
Noise-Induced Predictability Redistribution Across Forecast Horizons of Extreme Events in Chaotic Dynamics
Andrei Velichko, Viet-Thanh Pham
Extreme events (EEs) in chaotic dynamics are rare broad excursions whose forecastability can be altered by dynamical noise. We investigate how noise changes EE occurrence and predi…
When More Data Become Less Informative: Finite-Precision Periodicization and Collapse of Forecast-Error Lyapunov Estimates
Andrei Velichko, Viet-Thanh Pham
Largest Lyapunov exponents (LLEs) quantify exponential sensitivity, but data-driven estimates are often obtained from finite-precision trajectories. We show that increasing the len…
Equation-Free Period-Aware Forecast-Error Contraction for Estimating Negative Largest Lyapunov Exponents from Short Trajectory Ensembles
Andrei Velichko, N'Gbo N'Gbo, Viet-Thanh Pham
Estimating positive largest Lyapunov exponents from data is comparatively natural because neighboring trajectories separate, whereas stable dynamics require resolving contraction b…