Time-Series Models: Analyzing Skills Beyond the Hype

Large language models (LLMs) and their time-series counterparts (TSLMs) are transforming how we approach time-series question answering (TSQA). Unlike traditional text-only question answering, TSQA challenges models to interpret temporal signals with precision. These signals might appear at varying scales, specific times, or across different intervals. However, existing benchmarks fall short, often categorizing tasks by type rather than by the underlying skills needed to address them.

Introducing TS-Skill

Enter TS-Skill, a novel benchmark explicitly designed to diagnose these skills. It breaks down TSQA into three essential capabilities: temporal scale selection, temporal localization, and cross-interval integration. Here's what the benchmarks actually show: the ability to pick the right temporal scale (SK1), pinpoint precise temporal locations (SK2), and integrate information across time intervals (SK3).

TS-Skill offers a structured approach by providing timestamp-aware questions and drawing from a broad range of domains. The benchmark's questions undergo human validation to ensure quality. An innovative skill-guided framework, SKEvol, drives this benchmark. This framework combines domain-aware seed generation, skill-focused question crafting, metadata and code-supported answer building, and verified by multi-phase human-in-the-loop processes.

Capability Gaps Revealed

Tests on ten latest LLMs and TSLMs reveal significant and uneven gaps in these skill areas. Notably, SK3, or cross-interval integration, remains a consistent challenge. The reality is, while tool-augmented agents perform better on standalone SK3 tasks, many non-agent models struggle. Strip away the marketing and you get an honest look at a major blind spot in the current generation of models.

Why should this matter? Because in practical applications, like financial forecasting or climate modeling, failing to integrate across intervals could mean missing critical temporal patterns. If these models can't handle this, can they be trusted for decisions where temporal accuracy is vital?

Rethinking Model Evaluation

These findings suggest a broader issue: our traditional benchmarks might obscure critical weaknesses in temporal reasoning. By focusing on skill-level evaluations, we can identify failures that aggregate scores obscure. The architecture matters more than the parameter count temporal reasoning. As we push for more sophisticated applications of AI, ignoring these gaps isn't just a technical oversight, it's a potential risk to industries relying on precise temporal predictions.

So, what's the next step? More granular benchmarks like TS-Skill should become the norm. Only then can we truly understand and improve the temporal reasoning capabilities of our AI models.