Harnessing Generalist Agents for Contextualized Time Series
Quick Answer
TimeClaw is a new framework that enhances generalist LLM agents for contextualized time series analysis, integrating executable tools and multimodal memory.
Quick Take
Extensive evaluations across energy, finance, and weather domains show improved performance, enabling better temporal reasoning. The framework supports end-to-end workflows, addressing the need for holistic modeling in real-world applications.
Key Points
- TimeClaw equips agents with time series-native runtime support for enhanced reasoning.
- Integrates executable temporal tools for grounded and auditable analysis.
- Features episodic multimodal memory for retrieving relevant reasoning traces.
- Demonstrated improved performance across diverse benchmarks in real-world domains.
- Code available at GitHub for further exploration and implementation.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 05404v1 Announce Type: new Abstract: Time series are often embedded in rich contexts that are essential for holistic modeling. Moreover, real-world practitioners often require end-to-end workflows for analyzing temporal dynamics, where widely studied tasks such as forecasting are only one step in a broader solution loop.
While generalist AI agents offer a promising interface for such workflows under complex contexts, they still operate primarily in textual spaces that are not fully aligned with structured temporal signals. In this work, we introduce TimeClaw, an agentic harness framework for time series that equips generalist agents with the time series-native runtime support needed for contextualized temporal reasoning.
TimeClaw integrates executable temporal tools for grounded and auditable analysis, experience-driven capability evolution for creating reusable analytical routines, and episodic multimodal memory for retrieving relevant reasoning traces. Together, these components unlock harnessed open-ended temporal reasoning with contextual information.
Extensive evaluation on multiple benchmarks covering diverse tasks across energy, finance, weather, traffic, and other real-world domains demonstrates improved performance of TimeClaw. Code is available at https://github. com/iDEA-iSAIL-Lab-UIUC/TimeClaw.
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