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Time Series FMs show surprising blind spots in basic temporal reasoning

By PulseAugur Editorial · [1 sources] ·

A new research paper titled "Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMs" highlights significant limitations in current Time Series Foundation Models (TSFMs). Despite their success on broad benchmarks, models like Chronos-2, Moirai, and Toto frequently fail to accurately forecast basic temporal patterns such as trends and periodic signals, even when provided with exogenous covariates. While fine-tuning can improve performance on specific tasks, it often degrades performance on others, indicating a lack of generalizable foundational capabilities. These failures extend to real-world sensor forecasting, where TSFMs underutilize available leading indicators, limiting their practical utility. AI

IMPACT Highlights a gap in current TSFMs' ability to generalize basic temporal reasoning, potentially limiting their real-world application and reliability.

RANK_REASON Research paper published on arXiv detailing limitations of Time Series Foundation Models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Time Series FMs show surprising blind spots in basic temporal reasoning

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Research paper published on arXiv detailing limitations of Time Series Foundation Models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nafiseh Ghoroghchian, Haipeng Zhang, Shuyi Han, Alex Labach, George Stein ·

    Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMs

    arXiv:2610.02058v1 Announce Type: new Abstract: Despite the success of Time Series Foundation Models (TSFMs) on broad benchmarks, their ability to internalize basic temporal logic, especially in settings supported by exogenous covariates, remains under-examined. We introduce Simp…

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