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Adaptive Nonlinear Vector Autoregression: Robust Forecasting for Noisy Chaotic Time Series

A data-adaptive NVAR model that combines delay-embedded linear inputs with features generated by a shallow, trainable multilayer perceptron (MLP) is proposed, enabling the model to learn data-driven nonlinearities, while preserving a simple readout structure and improving scalability.

Sherkhon Azimov, S. López-Moreno, E. Dolores-Cuenca et al. · 2 citations

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