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
Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual prompts for modality alignment-introducing nontrivial computational overhead and failing to leverage the rich spectral dynamics inherent in time-series data.
arXiv:2608. 11623v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting.
By Rentao Gu, Yihang Ding, Junjie Li, Yi Ding, Weijing Sang, Xiaoli Huo, Xin Qin, Yuefeng Ji
arXiv:2606. 06010v1 Announce Type: new Abstract: Long-term time series forecasting benefits from inductive biases that expose recurring temporal structure.
By Zhangyao Song, Ziqiong Li, Xiangfei Qiu, Chao Zha, Yinfei Xu, Tao Guo
arXiv:2608. 04051v1 Announce Type: new Abstract: Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows.
By Jung Min Choi, Vijaya Krishna yalavarthi, Lars Schmidt-Thieme