arXiv:2608. 04695v1 Announce Type: cross Abstract: Federated adaptation of time-series foundation models (TSFMs) is attractive for building energy forecasting because meter data are private, distributed, and highly non-IID.
By Priyanka Nihalchandani, Naman Srivastava, Varun Ojha, Pandarasamy Arjunan
arXiv:2606. 11625v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) are increasingly explored as predictive experts within emerging agentic time-series systems.
By Kanghui Ning, Yushan Jiang, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
arXiv:2601. 16632v4 Announce Type: replace-cross Abstract: Time series forecasting has witnessed significant progress with deep learning.
By Haonan Yang, Jianchao Tang, Zhuo Li
arXiv:2607. 06607v1 Announce Type: cross Abstract: Accurate long-term forecasting in complex systems is frequently compromised by dataset-level distribution shifts, where diverse underlying behavioral modes and evolving system states drive the dynamic multivariate time-series.
By Lanhao Li, Bingshu Xie, Lijun Sun, Xin Xue, Haoyi Zhou, Jianxin Li
OceanMoE is a structured conditional sparse Mixture-of-Experts framework designed for long‑horizon multivariate ocean forecasting. It fuses cross‑variable information to build target‑specific local representations and performs content‑conditioned sparse routing at each spatial location, with the number of active experts adjusted by router confidence. Experiments on ORAS5 data show that OceanMoE reduces aggregate forecasting error and maintains lower geometric‑mean normalized RMSE compared to baselines, while expert allocation varies with prediction targets and locations.
By Yishun Zhu, Jian Wang
Time-series foundation models (TSFMs) are increasingly explored as predictive experts within emerging agentic time-series systems. However, TSFMs exhibit heterogeneous inductive biases, and no single model consistently dominates across forecasting regimes, making expert selection a critical challenge.