Benchmarking System One Models for Time-Series Decision Making: A Case Study of Jev

Authors: Jianye Wang , Kexin Zhang , Jiayin Xiong , Hao Xue

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Abstract: Time-series tasks require both accurate predictions and fast decisions. Advances in large models have introduced decision-oriented models that directly output answers and their probabilities, which we call Structured System 1 models. Their applicability to time-series tasks remains underexplored. Using Jev 1.13.0 as a case study, we evaluate time-series classification, event type prediction, oil temperature direction and stock movement prediction, converting continuous forecasting targets into discrete decisions to fit the model interface. We establish a unified evaluation framework using accuracy and AUC. Results show stronger performance on selected explicit temporal patterns and event type prediction, but near-chance discrimination on many numerical sequence classification tasks and stock movement prediction. Some datasets also reveal a gap between ranking quality and final decision accuracy. These findings motivate improvements in temporal representation, domain adaptation, and decision-score alignment. Standardized sample construction, model calls, and result recording support extending the evaluation to future models.

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13 pages, 2 figures. Code: this https URL

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Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG)

Cite as:

arXiv:2610.33410 [cs.AI]

(or

arXiv:2610.33410v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33410

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arXiv-issued DOI via DataCite (pending registration)

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From: Jianye Wang [ view email ]

[v1]

Sat, 24 Oct 2026 02:58:51 UTC (278 KB)

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