Timer‑M1 is a pretrained multivariate time‑series foundation model that learns with primitives for zero‑shot forecasting. It uses a primitive‑based data synthesis and pretraining pipeline to generate series with shared temporal and relational patterns, assembling them into multivariate samples with distinct channel roles. The model employs gated two‑dimensional Transformer blocks that dynamically allocate cross‑variate attention, achieving top performance on several large‑scale forecasting benchmarks.
By Haoran Zhang, Haixuan Liu, Xingjian Su, Yong Liu, Zhi Chen, Yuxuan Wang, Jianmin Wang, Mingsheng Long
The paper introduces a vertical federated learning framework that learns one‑step‑ahead cross‑client temporal interdependencies in decentralized industrial systems with heterogeneous, proprietary models. Each client compresses high‑dimensional sensor data into low‑dimensional latent states via a nonlinear state‑space model, while a central Graph Attention Network (GAT) learns a state‑transition model and derives attention coefficients that reflect cross‑client interactions. The authors analytically link these attention coefficients to the Jacobian of the transition model, proving bounded closeness to a centralized oracle, and validate the approach with synthetic and real‑world experiments showing improved federated root‑cause analysis.
By Ayse Tursucular, Ayush Mohanty, Nazal Mohamed, Nagi Gebraeel
AdaMamba introduces an adaptive frequency-gated Mamba architecture for long‑term time series forecasting. The model integrates an interaction learning module that captures cross‑variable dependencies and multi‑scale context, and an adaptive frequency‑gated state space module that generates context‑dependent frequency bases and unifies temporal forgetting into a single time‑frequency gate. Experiments on seven public benchmarks and four domain‑specific datasets show that AdaMamba consistently outperforms state‑of‑the‑art baselines while remaining computationally efficient.
By Xudong Jiang, Mingshan Loo, Wengen Li, Mingrui Zhang, Jihong Guan, Shuigeng Zhou
arXiv:2606. 07291v1 Announce Type: new Abstract: Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspondences.
By Tao Chen, Yexu Zhou, Zhi Gong, Hengwei He, Hongda Li, Zhewei Chen, Dongjing Wang, Xin Zhang, Decheng Liu, Chunlei Peng, Zheng Chen, Wenyue Ding
arXiv:2606. 01289v1 Announce Type: new Abstract: Zero-shot time series forecasting aims to predict future values for previously unseen series, requiring models to generalize temporal dynamics beyond the training distribution.
By Yifan Wu, Junjie Wu, Kai Wu, Xiaoyu Zhang, Jian Lou
Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deployment. Bridging this gap requires further intervention to handle domain shift, task heterogeneity, limited supervision, and computational constraints, which motivates post-training as a broad class of methods to adapt, augment, compose, calibrate, or specialize pretrained TSFMs for downstream tasks.
The article introduces rc_prediction, a Python package that implements parameter‑aware reservoir computing (PARC) for modeling nonlinear dynamical systems when governing equations are unknown. PARC trains a reservoir computer on time‑series data collected at multiple parameter values, then uses the trained model in closed‑loop to generate trajectories at unseen parameter settings, enabling parameter scans and autonomous prediction. The package offers hyperparameter optimization, optional GPU acceleration via PyTorch, and demonstrates its use on three benchmark systems, including predicting critical transitions and system collapse from pre‑critical data.
By Jin-Cheng Rao, Zheng-Meng Zhai, Ying-Cheng Lai
arXiv:2609.06006v1 Announce Type: cross
Abstract: Deep learning for time series has progressed through successive architectural paradigms, from recurrent networks and transformers to structured state...
By Minh Hoang Nguyen, Huu Hiep Nguyen, Manh Nguyen, Van Dai Do, Dung Nguyen, Hung Le
SMart is a new time series representation learning framework that combines a multi-phase recurrence plot recovery task with a source dataset selector. The recovery task uses three alternative modes to guide the encoder in capturing time series dynamics, while the selector chooses multiple suitable source datasets to augment the target dataset during pre‑training. Experiments demonstrate that SMart surpasses state‑of‑the‑art models, reducing mean absolute error by up to 19.5% in regression and increasing classification accuracy by up to 1.34%.
By Fang He, Wang-chien Lee
arXiv:2610.17864v1 Announce Type: cross
Abstract: Long-term time series forecasting requires temporal dependency modeling that remains stable and generalizable under temporal distribution shifts. How...
By Yangze Li, Zhengyang Zhou, Qihe Huang, Pengkun Wang, Binwu Wang, Yang Wang
SwitchPFN introduces a shared projection and regime codebook for time‑series classification with tabular foundation models, preserving local temporal transitions while ensuring consistent feature definitions across samples. The method outperforms existing representations, achieving the highest mean accuracy on evaluated benchmarks and improving the strongest baseline by 4.47% relative. Ablation, sensitivity, and limited‑data experiments confirm the benefits of the proposed representation design.
By Zhenyi Zhu, Jacqueline Pang, Peilin Shen, Tianyi Song, Tingwei Zhang, Keyi Hu, Kangjun Yin, Shiwei Pu, Yingbo Zhou, Chen Shao
arXiv:2608. 06765v1 Announce Type: new Abstract: Continuous-time dynamic graph models predict future links by compressing past interactions into neural states.
By Minwoo Yu, Young-guk Ha