A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

2026-08-31

AI Summary

针对时间序列预测中模型固定及缺乏特定检查的问题，通过构建人机交互系统CastClaw结合专业模型和用户输入来提高预测准确性。

Abstract

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 connects data, specialized models, analytical tools, user input, and a versioned execution record in one runtime. Users specify the target, horizon, constraints, and hypotheses in natural language. Starting from a supplied or model-generated forecast, CastClaw checks temporal patterns and user constraints; when evidence is missing, it retrieves context, runs an analysis or another model, or asks the user. It then keeps, revises, or escalates the result under explicit stopping conditions. The output contains the final forecast and an execution report recording inputs, evidence, actions, and revisions. In this five-dataset electricity-price setting, CastClaw reports the lowest point-estimate MSE and MAE among 16 baselines. A Nord Pool case demonstrates the inspectable workflow. CastClaw was also validated offline on provincial electricity-load data from North China covering January--June 2026.

Problem

• time-series forecasting
• human-in-the-loop
• domain expertise
• forecasting-specific checks
• constraints

Innovation

• Human-in-the-loop
• Autonomous Forecasting
• Time Series
• Constraint Checking
• Executable Record