3 papers
cs.LG2025
Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Ethan Baron, Boris Oreshkin, Ruijun Ma +5
Many time series applications require access to multi-step forecast trajectories in the form of sample paths. Recently, time series foundation models have leveraged multi-step look…
cs.LG2025
Using Pre-trained LLMs for Multivariate Time Series Forecasting
Malcolm L. Wolff, Shenghao Yang, Kari Torkkola +1
Pre-trained Large Language Models (LLMs) encapsulate large amounts of knowledge and take enormous amounts of compute to train. We make use of this resource, together with the obser…
cs.LG2024
Chronos: Learning the Language of Time Series
Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen +15
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a f…