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
AdaRDiff is a new adaptive reversible differencing technique for time‑series forecasting that learns weighted differencing to remove trend and seasonality, stabilizes residuals for forecasting, and then reconstructs the forecast autoregressively. The method offers a closed‑form convolutional implementation that can be GPU‑parallelized, achieving up to 33.7× speedup over naive recurrence. Experiments on eight diverse benchmarks show state‑of‑the‑art accuracy and significant performance gains when integrated into various backbone models, from linear models to Transformers.
By Morad Laglil, Younes Hlal, Marouane El Hadari, Emilie Devijver, Eric Gaussier
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
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. 20052v1 Announce Type: new Abstract: Probabilistic time series forecasting remains challenging, largely because modeling distinct trend and seasonal dynamics requires specialized approaches.
By Alexander Marusov, Dmitry Anikin, Alexey Zaytsev
arXiv:2606. 27282v1 Announce Type: new Abstract: Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy.
By Lang Huang, Jinglue Xu, Luke Darlow