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New benchmark CastWorld evaluates agentic time series forecasting

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced CastWorld, a new benchmark designed to evaluate agentic time series forecasting. This benchmark includes a diverse suite of datasets across eight domains, a modular harness with 49 tools to support adaptive interactions, and a comprehensive evaluation framework. CastWorld assesses not only predictive accuracy but also process execution, behavioral trustworthiness, and resource efficiency, offering a more holistic view of forecasting agent performance. AI

IMPACT Provides a standardized framework for evaluating the capabilities of AI agents in complex forecasting tasks.

RANK_REASON The item is a research paper introducing a new benchmark for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark CastWorld evaluates agentic time series forecasting

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The item is a research paper introducing a new benchmark for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bokai Pan, Ruijie Yang, Mingyue Cheng, Jiahao Wang, Xiaoyu Tao ·

    CastWorld: Benchmarking Context-Aware Agentic Time Series Forecasting Beyond Predictive Accuracy

    arXiv:2611.15262v1 Announce Type: cross Abstract: Agentic time series forecasting extends conventional approaches beyond one-shot numerical prediction toward autonomous, context-aware interaction. However, existing benchmarks offer limited integrated support for diverse contextua…

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