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Sherkhon Azimov

Publications and source records attributed to Sherkhon Azimov.

2 recordsLinked to original sources
arXiv2026

PCA-Enhanced Adaptive NVAR Framework for High-Resolution Sea Surface Temperature Forecasting in the East Sea

Accurate forecasting of sea surface temperature (SST) is essential for marine ecosystem monitoring, climate assessment, fisheries management, and operational ocean forecasting. While numerical ocean models provide reliable predictions, they are computationally expensive, and conventional machine learning methods often suffer from high-dimensional inputs and error accumulation during long-term autonomous forecasting. This study extends our previously proposed Adaptive Nonlinear Vector Autoregression (Adaptive NVAR) framework to high-resolution real-world SST prediction by integrating Principal Component Analysis (PCA) through Singular Value Decomposition (SVD). Daily SST fields from the GLORYS12V1 reanalysis dataset covering the East Sea, Yellow Sea, and East China Sea are compressed into a lower-dimensional latent representation that preserves the dominant spatial variability. The proposed reduced-order framework is evaluated using autonomous rolling forecasts up to a 90-day horizon and compared with Standard NVAR (Next Generation Reservoir Computing) and a Persistence baseline. Across all three regions, Adaptive NVAR consistently suppresses long-term error accumulation, achieving up to 96.52% improvement in mean squared error relative to Persistence while maintaining stable predictive performance throughout extended forecasting. Although the adaptive architecture incurs a higher one-time offline optimization cost than Standard NVAR, inference is completed in milliseconds, making the proposed framework an efficient and scalable approach for long-term, high-resolution ocean state forecasting.

cs.LG↗
arXiv2025

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

Nonlinear vector autoregression (NVAR) and reservoir computing (RC) have shown promise in forecasting chaotic dynamical systems, such as the Lorenz-63 model and El Nino-Southern Oscillation. However, their reliance on fixed nonlinear transformations - polynomial expansions in NVAR or random feature maps in RC - limits their adaptability to high noise or complex real-world data. Furthermore, these methods also exhibit poor scalability in high-dimensional settings due to costly matrix inversion during optimization. We propose a data-adaptive NVAR model that combines delay-embedded linear inputs with features generated by a shallow, trainable multilayer perceptron (MLP). Unlike standard NVAR and RC models, the MLP and linear readout are jointly trained using gradient-based optimization, enabling the model to learn data-driven nonlinearities, while preserving a simple readout structure and improving scalability. Initial experiments across multiple chaotic systems, tested under noise-free and synthetically noisy conditions, showed that the adaptive model outperformed in predictive accuracy the standard NVAR, a leaky echo state network (ESN) - the most common RC model - and a hybrid ESN, thereby showing robust forecasting under noisy conditions.

cs.LG↗