Unverified50% confidenceFactExact time
TimesFM 2.5 uses a two-stage approach to handle covariates: first fitting the covariate effects with a regression outside the model, then letting the model predict the residuals.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/22/2026
First Seen
Valid until: 1/20/2027
Sources
Google TimesFM 3.0 Hands-On: Is Zero-Shot Forecasting Good Enough?
youtubePrompt Engineering10/22/2026
Related Entities
Related Claims
UnverifiedTime-Series Foundation Models are general-purpose forecasting models pretrained on large-scale, multi-domain time-series data with cross-task generalization capabilities, with representative works including TimesFM, Chronos, and Moirai.69% similarUnverifiedTimesFM supports not only point forecasting but also probabilistic forecasting (outputting quantiles of the predicted distribution).69% similarUnverifiedTimesFM 3.0 is a foundation model dedicated to time series forecasting, with zero-shot forecasting capability.68% similarUnverifiedThe paper "Twice Upon a Time: Complementary Representations for Time-Series Reasoning" proposes the design principle of providing time-series language models (TSLMs) with two complementary representations simultaneously: line graphs and normalized delay embeddings.67% similarUnverifiedMost existing time series foundation models are built on regularly sampled sequences with uniformly distributed time steps by default, and have limited generalization capability when faced with irregular time intervals and asynchronous cross-variable dependencies.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1085838API
curl https://kongchang.com/api/v1/knowledge/claims/1085838MCP
get_claim(id=1085838)