4 papers
Scale-Invariant Training for Time Series Foundation Models
Ignacy Stepka, Willa Potosnak, Kin G. Olivares +1
Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains. This setting exposes models to series who…
Towards Universal Representation-Based Process Control
Jinmyeong Choi, Taesup Kim, Artur Dubrawski
Many temporal process learning and monitoring pipelines operate in local windows, making window-level decisions unavoidable in practice. In such settings, classical statistical tes…
Non-Stationarity in the Embedding Space of Time Series Foundation Models
Jinmyeong Choi, Brad Shook, Artur Dubrawski
Time series foundation models (TSFMs) are widely used as generic feature extractors, yet the notion of non-stationarity in their embedding spaces remains poorly understood. Recent…
Global Deep Forecasting with Patient-Specific Pharmacokinetics
Willa Potosnak, Cristian Challu, Kin G. Olivares +2
Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, it can be challenging in practice due to variable medicati…