arXiv:2605.24703v1 Announce Type: new Abstract: Large language models (LLMs) and time-series language models (TSLMs) are increasingly applied to time-series question answering (TSQA). Unlike text-only QA, TSQA requires models to ground answers in temporal signals whose patterns may occur at different scales, specific time locations, or across separated intervals. However, existing benchmarks are typically organized by task types or high-level reasoning categories, making it difficult to diagnose the underlying signal-level capabilities driving model performance. We introduce TS-Skill, a contro
The increasing application of large language models (LLMs) and time-series language models (TSLMs) to time-series question answering necessitates more robust evaluation benchmarks.
This benchmark addresses a critical gap in assessing AI model capabilities in processing temporal data, vital for applications ranging from finance to climate modeling.
The introduction of TS-Skill provides a standardized framework to diagnose signal-level capabilities of AI models, moving beyond high-level reasoning categories.
- · AI developers
- · Time-series data analysts
- · Academic researchers
- · Enterprises using time-series AI
- · Models with poor temporal understanding
Improved evaluation leads to more effective and reliable AI models for time series analysis.
Enhanced trust in AI systems for critical temporal applications, potentially accelerating their adoption in sensitive sectors.
The ability to accurately forecast and react to complex temporal patterns could yield significant economic advantages for early adopters.
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Read at arXiv cs.CL