arXiv AI
Oct 9

Timer-M1: A Multivariate Time Series Foundation Model via Learning Primitives

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
arXiv AI
1d ago

AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting

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 Machine Learning
Oct 12

HART: Hybrid Attention-RWKV Time series Foundation Model

The paper introduces HART, a Hybrid Attention-RWKV Time-series Foundation Model that alternates attention and RWKV blocks to improve long-horizon forecasting. It identifies a problem in RWKV-7 called Placeholder Idling Decay, where repeated state transitions degrade historical information during non-autoregressive multi-step forecasting. Experiments on GIFT-Eval and TIME-Benchmark show that HART outperforms pure RWKV on medium and long horizons while matching short-horizon performance, and achieves comparable long-horizon MASE to a much larger Transformer with only 36 M parameters.

By Qingwen Lin, Boyan Xu, Shifeng Xie, Zhifeng Hao, Keli Zhang, Ruichu Cai
arXiv Machine Learning
Aug 7

Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations

arXiv:2608. 06107v1 Announce Type: new Abstract: Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fast, differentiable surrogate models.

By Guillaume Couairon, Alexis Jacq, Yu-Han Wu, Renu Singh, Yana Hasson, Quentin Berthet, Romuald Elie