arXiv:2607. 06504v1 Announce Type: new Abstract: Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot generalization.
By Qian Sun, Yong-Ming Tian, Jia-Wei Huang, Cheng Feng, Shao-Qun Zhang
The paper introduces SimpleTimeBench, a diagnostic suite testing basic temporal primitives like monotonic trends, periodic signals, and leading indicator covariates. It finds that prominent multivariate Time Series Foundation Models (Chronos‑2, Moirai, and Toto) often produce suboptimal zero‑shot forecasts for these simple patterns, and that fine‑tuning can improve specific tasks while harming performance on other fundamentals. These failures persist in real‑world sensor forecasting, indicating that current TSFMs may lack the inductive biases needed to capture straightforward relationships, thereby limiting their practical reliability.
By Nafiseh Ghoroghchian, Haipeng Zhang, Shuyi Han, Alex Labach, George Stein
arXiv:2607. 01204v1 Announce Type: new Abstract: We introduce TiRex-2, a recurrent xLSTM-based time series foundation model that generalizes the univariate TiRex to multivariate forecasting with both past and future covariates.
By Patrick Podest, Marco Pichler, Elias B\"urger, Levente Z\'olyomi, Bernhard Voggenberger, Wilhelm Berghammer, Daniel Klotz, Sebastian B\"ock, G\"unter Klambauer, Sepp Hochreiter
arXiv:2606. 10798v1 Announce Type: new Abstract: Pretrained time series foundation models (TSFMs) have enabled zero-shot forecasting on unseen target series.
By Yosuke Yamaguchi, Issei Suemitsu, Yuki Kajihara, Wenpeng Wei
QUALS is a large‑scale time‑series corpus equilibrium framework designed to improve data efficiency for zero‑shot forecasting. It uses pattern quantization to decode heterogeneous patterns from mixed corpora and a learnability synchronization mechanism to calibrate sampling weights, bridging the optimization gap between simple and complex motifs. Benchmarks show that pre‑training on QUALS yields superior zero‑shot performance even with reduced training budgets.
By Yujie Li, Zezhi Shao, Chengqing Yu, Yisong Fu, Weijie Zhu, Yifan Du, Jilin Hu, Bin Yang, Yongjun Xu, Fei Wang
arXiv:2602. 12147v4 Announce Type: replace Abstract: Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation.
By Zhongzheng Qiao, Sheng Pan, Anni Wang, Viktoriya Zhukova, Yong Liu, Xudong Jiang, Qingsong Wen, Mingsheng Long, Ming Jin, Chenghao Liu