Hugging Face Trending Papers
Timestral
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
3d ago
Timestral
Timestral is a family of four time‑series foundation models (5M, 28M, 123M, and 209M parameters) designed for probabilistic forecasting. It achieves state‑of‑the‑art results on GIFT‑Eval, TIME, and fev‑bench, with the 209M and 123M variants topping every metric. The approach relies on Decoupled Horizon Forecasting, a covariate framework for known‑future variables, and a two‑stage training pipeline that first pre‑trains on synthetic data before fine‑tuning on real and synthetic data with extended context.
arXiv Machine Learning
Sep 15
Tabby: An Open Pretraining Recipe for Time Series Foundation Models
arXiv:2609.13956v1 Announce Type: new Abstract: In this report, we release Tabby, a long context probabilistic time series foundation model, together with a complete and open recipe of how it was bui...
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.
arXiv AI
Oct 12
KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting
KITE is a knowledge‑injected temporal encoder that augments self‑supervised event forecasting with semantic priors supplied by system engineers. It uses two switchable mechanisms—a covariate mechanism and an attention‑bias mechanism—so the same architecture can incorporate different prior types without per‑dataset tuning. Experiments show that informative priors improve performance in short contexts and enable out‑of‑domain transfer, while priors already present in the observed window have little effect.
Hugging Face Trending Papers
Sep 28
Instance-Adaptive Prompts as Context for Time-Series Foundation Models
The paper introduces PaCTS, a method that generates instance‑adaptive latent prompts—continuous embedding tokens—to provide compact contextual information for frozen time‑series foundation models (TSFMs). These prompts are constructed from instance‑specific global statistics and refined with segment‑level temporal data, enabling the model to capture both global characteristics and local temporal variations. Experiments show that PaCTS improves forecasting performance across various context lengths and model architectures, often outperforming the same backbone with double the context while reducing inference computation, and it also offers stronger improvements and better out‑of‑distribution generalization compared to weight‑space adaptation methods.
arXiv Machine Learning
Jul 28
Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting
arXiv:2607. 23146v1 Announce Type: new Abstract: Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecasting, enabling accurate predictions on datasets never seen during pre-training.