arXiv cs.AI· Jianye Wang, Kexin Zhang, Jiayin Xiong, Hao Xue·· 12 小时前AI 评分24
以 Jev 1.13.0 为例，评测 System 1 模型的时间序列决策能力
Benchmarking System One Models for Time-Series Decision Making: A Case Study of Jev
AI 导读
研究以 Jev 1.13.0 为案例，评测 Structured System 1 模型在时间序列分类、事件类型预测、油温方向与股票走势预测上的表现，将连续预测目标转为离散决策以适配模型接口，并用准确率与 AUC 建立统一评测框架。结果显示模型在部分显式时间模式与事件类型预测上表现较强，但在许多数值序列分类任务和股票走势预测上接近随机水平，部分数据集还出现排序质量与最终决策准确率之间的差距。
正文
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.
| Comments: | 13 pages, 2 figures. Code: this https URL |
| Subjects: | 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 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jianye Wang [view email]
[v1]
Sat, 24 Oct 2026 02:58:51 UTC (278 KB)
来源：arXiv cs.AI · arxiv.org