A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting
Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate domain expertise, assess prediction plausibility, and communicate uncertainty. Specialized forecasting models provide strong numerical predictions but usually operate in fixed pipelines, while general-purpose large language model (LLM) agents often lack forecasting-specific checks, constraints, and stopping rules. We present CastClaw, a human-in-the-loop autonomous forecasting system built through forecasting-oriented harness engineering. CastClaw c
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “A Human-in-the-Loop Autonomous Agent for Industry Time Serie” ≈ “AgentCore-8B””
- PossiblePossibly related (embedding) · 53%Building trade assistant: How Jefferies optimized front office trading operations with AI →
- FuzzySimilar title/name (fuzzy) · 87%SWE-agent/SWE-agent →
“Fuzzy title match (0.94): “A Human-in-the-Loop Autonomous Agent for Industry Time Serie” ≈ “SWE-agent/SWE-agent””
- FuzzySimilar title/name (fuzzy) · 87%zhayujie/CowAgent →
“Fuzzy title match (0.94): “A Human-in-the-Loop Autonomous Agent for Industry Time Serie” ≈ “zhayujie/CowAgent””
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%DietrichGebert/ponytail →
“Shared author/contributor keys: cheng”
- PossiblePossibly related (embedding) · 49%Real-Time Intelligence with IBM Time Series Models on Confluent →