TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
Large Language Models (LLMs) have shown promising performance in time series modeling tasks, but do they truly understand time series data? While multiple benchmarks have been proposed to answer this fundamental question, most are manually curated and focus on narrow domains or specific skill sets. To address this limitation, we propose scalable methods for creating comprehensive time series reasoning benchmarks that combine the flexibility of templates with the creativity of LLM agents. We first develop TimeSeriesExam, a multiple-choice benchmark using synthetic time series to evaluate LLMs across five core reasoning categories: pattern recognitionnoise understandingsimilarity analysisan...
| Authors | Malgorzata Gwiazda, Yifu Cai, Mononito Goswami et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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