CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting
Researchers introduce CastFlow, a dynamic agentic framework that applies large language models to time series forecasting through multi-stage workflows combining planning, action, and reflection. The system uses role-specialized agents—a general-purpose LLM paired with a fine-tuned domain-specific model—to iteratively refine forecasts using ensemble methods and contextual memory, demonstrating superior performance over existing static generative approaches.
CastFlow represents a meaningful advancement in applying LLMs to time series forecasting by moving beyond single-pass generative models to iterative, agent-based workflows. The framework addresses fundamental constraints in existing LLM-based forecasting: limited temporal pattern extraction, single-round feature acquisition, and lack of ensemble integration. By restructuring forecasting as a multi-stage agentic process involving planning, execution, forecasting, and reflection, CastFlow enables continuous refinement rather than one-shot prediction.
The technical innovation centers on role specialization, where a frozen general-purpose LLM handles reasoning tasks while a fine-tuned domain-specific model performs numerical forecasting guided by ensemble baselines. This hybrid approach avoids catastrophic forgetting while leveraging specialized domain knowledge. The two-stage training methodology combining supervised fine-tuning with reinforcement learning using verifiable rewards creates measurable optimization paths for the domain-specific component.
For the broader AI and financial forecasting sectors, CastFlow demonstrates that agentic workflows can outperform simpler generative paradigms, suggesting a shift toward more adaptive AI systems. This matters particularly for quantitative finance, energy forecasting, and supply chain planning where accuracy improvements directly impact operational decisions and profitability. The memory module and multi-view toolkit approach also provides a template for incorporating external data sources and historical patterns.
Future developments should focus on computational efficiency—agentic workflows are inherently more expensive than single-pass models—and real-world deployment across diverse time series domains. The framework's effectiveness on various datasets hints at generalizability, but production performance metrics and comparison against domain-specific statistical models remain critical validation points.
- →CastFlow introduces agentic workflows to time series forecasting, enabling multi-round refinement versus single-pass prediction generation
- →Role-specialized design pairs frozen general-purpose LLMs with fine-tuned domain models to balance reasoning capability and numerical accuracy
- →Memory modules and multi-view toolkits provide ensemble forecasting baselines and contextual feature acquisition across multiple rounds
- →Two-stage training combining supervised fine-tuning and reinforcement learning with verifiable rewards optimizes domain-specific model performance
- →Framework demonstrates superior results across diverse datasets, suggesting practical applicability to financial, energy, and supply chain forecasting