The paper introduces a data‑adaptive nonlinear vector autoregression (NVAR) model that replaces fixed polynomial or random feature maps with a shallow, trainable multilayer perceptron (MLP). By jointly training the MLP and a linear readout via gradient‑based optimization, the model learns data‑driven nonlinearities while maintaining a simple readout structure, improving scalability in high‑dimensional settings. Experiments on several chaotic systems, both noise‑free and synthetically noisy, show that this adaptive NVAR outperforms standard NVAR, a leaky echo state network (ESN), and a hybrid ESN in predictive accuracy, demonstrating robust forecasting under noisy conditions.
By Sherkhon Azimov, Susana Lopez-Moreno, Eric Dolores-Cuenca, Sieun Lee, Jae-Il Kwon, Sangil Kim
BEAT (Balanced Frequency Adaptive Tuning) is a framework for long‑term time‑series forecasting that uses frequency‑specific error monitoring and adaptive gradient modulation. It introduces a Frequency‑Specific Monitor to compare predicted and target wavelet coefficients in a normalized space, and a Dynamical Gradient Balancer that assigns larger gradient weights to components with higher relative errors. Experiments on seven real‑world datasets demonstrate that BEAT achieves competitive performance against state‑of‑the‑art forecasting methods.
By Zhixuan Li, Naipeng Chen, Seonghwa Choi, Sanghoon Lee, Weisi Lin
The paper introduces SAFE, an SNR‑adaptive frequency estimator that combines a time‑frequency image neural network (TFINet) to enhance weak tone components at low SNR with an SNR‑based frequency selector (SFS) that chooses the appropriate estimator for each tone. SAFE achieves a 13.00% false negative rate across –10 dB to 0 dB SNR, improving over state‑of‑the‑art methods by 13.04% and reducing nearest‑neighbor RMSE by 56.67%. Real‑world experiments confirm its robustness in cluttered environments.
By Hee-Yang Jung, Dong-Hee Paek, Woo-Jin Jung, Seung-Hyun Kong
arXiv:2501. 10870v2 Announce Type: replace-cross Abstract: The principal objective of this work is twofold within nonparametric regression settings: (1) to establish the minimax optimal convergence rates for fixed-bandwidth Gaussian kernel spectral algorithms when the true regression function resides in a Sobolev space, and (2) to apply Gaussian spectral algorithms for achieving robust and adaptive transfer learning under concept shift.
By Haotian Lin, Matthew Reimherr
arXiv:2606. 01339v1 Announce Type: cross Abstract: Long-term time-series forecasting needs models that are accurate yet efficient enough for commodity hardware.
By Mirza Samad Ahmed Baiga, Syeda Anshrah Gillani
arXiv:2606. 01306v1 Announce Type: new Abstract: While Transformer-based architectures have established themselves as a dominant paradigm in Multivariate Time Series Forecasting (MTSF), their core self-attention mechanism inherently functions as a low-pass filter, systematically smoothing out high-frequency signals vital for sharp local changes.
By Peng He, Yao Liu, Yanglei Gan, Run Lin, Yuxiang Cai, Qiao Liu