CastClaw: A Human-Machine Collaborative System for Agentic Time Series Forecasting

Time series forecasting is commonly formulated as a model-centric and single-pass prediction task. However, real-world forecasting often requires task understanding, data diagnosis, contextual feature acquisition, tool-assisted modeling, reflective verification, and human feedback. In this paper, we demonstrate CastClaw, an interactive agent system for context-aware time series forecasting. CastClaw organizes forecasting as a structured runtime workflow that includes intent understanding, data profiling, iterative prediction, reflective verification, and traceable report generation. Supported by a tool library, an execution environment, and state management, CastClaw can invoke forecasting tools, compare models across different families and forecasting skills, track intermediate states, and incorporate user feedback. Through demonstrations on real-world forecasting scenarios, CastClaw shows how forecasting systems can move beyond static predictors toward interactive, evidence-grounded, and verifiable forecasting. Our code is available at (https://github.com/ustc-time-series/CastClaw). The demonstration video could be found at the link (https://ustc-time-series.github.io/cast-claw/).