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
arXiv:2606. 27908v1 Announce Type: new Abstract: Long-term time series forecasting finds extensive applications in domains such as power demand, traffic flow, meteorological observation, and renewable energy dispatch.
By Wenchao Liu, Hongbing Wang, Youji Zhu, Xiaodong Liu, Xiangguang Xiong
arXiv:2510. 05589v3 Announce Type: replace-cross Abstract: Effective time series forecasting enables various real-world applications, benefiting from the proliferation of mobile devices.
By Kangjia Yan, Chenxi Liu, Hao Miao, Xinle Wu, Yan Zhao, Chenjuan Guo, Bin Yang
arXiv:2506.12809v2 Announce Type: replace
Abstract: The long horizon forecasting (LHF) problem has come up in the time series literature for over the last 35 years or so. This review covers aspects o...
By Hans Krupakar, Kandappan V A
arXiv:2605. 15690v2 Announce Type: replace Abstract: Accurate and efficient long-term multivariate time series forecasting requires capturing recurring temporal structure while keeping inference cheap across many variables and horizons.
By Qingyuan Yang, Dongyue Chen, Da Teng, Junhua Xiao, Jiaji Pan, Shizhuo Deng
arXiv:2605. 07476v2 Announce Type: replace Abstract: Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variables.
By Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
In time-series forecasting tasks, the use of recursive forecasting on high-dimensional data substantially increases the complexity of the prediction process. The main challenge is that long-term dynam...