Embedding spaces of time series foundation models make mean shifts, variance changes, and trends linearly detectable, but detection degrades smoothly with shift strength and shows model-specific failure modes.
5 ICLR 2026 Workshop on Time Series in the Age of Large Models (TSALM) Denis Kwiatkowski, Peter CB Phillips, Peter Schmidt, and Yongcheol Shin
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Non-Stationarity in the Embedding Space of Time Series Foundation Models
Embedding spaces of time series foundation models make mean shifts, variance changes, and trends linearly detectable, but detection degrades smoothly with shift strength and shows model-specific failure modes.