LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics
Comprehensive understanding of time series remains a significant challenge for Large Language Models (LLMs). Current research is hindered by fragmented task definitions and benchmarks with inherent ambiguities, precluding rigorous evaluation and the development of unified Time Series Reasoning Models(TSRMs). To bridge this gap, we formalize Time Series Reasoning (TSR) via a four-level taxonomy of increasing cognitive complexity. We introduce HiTSR, a hierarchical time series reasoning dataset comprising 83k samples with diverse task combinations and verified Chain-of-Thought (CoT) trajectories. Leveraging HiTSR, we propose LLaTiSA, a strong TSRM that integrates visualized patterns with pr...
| Authors | Yueyang Ding, HaoPeng Zhang, Rui Dai et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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