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

Nafiseh Ghoroghchian, Haipeng Zhang, Shuyi Han, Alex Labach, George Stein

Keywords: Time Series Foundation Models; Time Series Foreceasting; Diagnostic Benchmarking; Multivariate Time Series; Exogenous Covariates; Zero-shot Evaluation

TL;DR: We introduce a diagnostic "unit test" benchmark revealing that state-of-the-art time series foundation models fail at fundamental temporal reasoning and systematically ignore highly informative covariates.

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 SimpleTimeBench, a diagnostic univariate and multivariate ``unit test" suite for primitives such as monotonic trends, periodic signals and leading indicator covariates, scenarios where near-perfect forecasts should be trivial. Surprisingly, prominent multivariate TSFMs (Chronos-2, Moirai and Toto) frequently produce suboptimal zero-shot forecasts for these inputs. While fine-tuning Chronos-2 improves its behaviour on specific tasks, we show that this adaptation degrades performance on other fundamental patterns rather than enhancing its generalizable foundational capabilities. This reveals a gap between pre-training scale and basic temporal reasoning, suggesting that current TSFMs lack the inductive biases needed to capture simple predictable functions. We further demonstrate that these failures are not merely synthetic curiosities: they persist in real-world sensor forecasting, where TSFMs consistently underutilize leading indicators available in observed covariates. This inability to capture simple relationships limits the practical utility and reliability of current multivariate models.

Submission Number: 132