arXiv AI August 18, 2026 By Armin Steinhauser

TinyCast: Probabilistic Zero-Shot Forecasting with Computed Periodicity

arXiv:2608. 15767v1 Announce Type: cross Abstract: We introduce TinyCast, an attention-free zero-shot forecaster that emits a predictive distribution from 146,505 parameters, on the premise that at this size the periodic structure of a context is worth computing rather than learning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 28

Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting

arXiv:2602. 17634v2 Announce Type: replace-cross Abstract: Learning time series foundation models has been shown to be a promising approach for zero-shot time series forecasting across diverse time series domains.

By Xinghong Fu, Yanhong Li, Georgios Papaioannou, Yoon Kim

llms

arXiv Machine Learning

Jun 2

Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting

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

llms

arXiv Machine Learning

Jun 10

CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting

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

llms benchmarks

arXiv Machine Learning

Jun 9

Zero and Few Shot Load Forecasting with Large Language Models

arXiv:2411. 11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios.

By Wenlong Liao, Chengrui Zhang, Zhe Yang, Mengshuo Jia, Christian Rehtanz, Jiannong Fang, Fernando Port\'e-Agel

llms nlp fine-tuning

arXiv AI

6d ago

QUAL: Quantile Alignment Improves Zero-Shot Time-Series Foundation Models

The paper introduces QUAL, a label‑free framework that improves zero‑shot time‑series foundation models by aligning their quantile forecasts using a pool of models as a probability reference. QUAL first aligns member forecasts, then aggregates them with Huber aggregation and rearrangement to produce a robust combined forecast without any training or model modification. Experiments on GIFT‑Eval show that this quantile alignment reduces average member MASE by 7.4% and CRPS by 9.8%, and the method remains robust even when weak forecasters are included in the candidate pool.

By Junhyeok Kang, Sangjun Han, Jun Seo, Seunghan Lee, Jaehoon Lee, Dongwan Kang, Minjae Kim, Sungdong Yoo, Tae Yoon Lim, Hwanil Choi, Soonyoung Lee, Wonbin Ahn

safety

arXiv Machine Learning

Sep 23

Evaluating Accuracy and Probabilistic Reliability of Zero-Shot Time Series Foundation Models

arXiv:2609.25788v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) promise a paradigm shift toward zero-shot forecasting by eliminating task-specific training. However, existing wo...

By Panagiotis Michael, Moysis Symeonides, Demetris Trihinas

llms benchmarks

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Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting

Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting

CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting

Zero and Few Shot Load Forecasting with Large Language Models

QUAL: Quantile Alignment Improves Zero-Shot Time-Series Foundation Models

Evaluating Accuracy and Probabilistic Reliability of Zero-Shot Time Series Foundation Models