Forecast First, Ask Later: How DCATS Makes Time Series Smarter with LLMs
TL;DR for operators Forecasting teams usually ask the same question first: which model should we use? DCATS suggests a more operationally useful question: which related histories should this model learn from? The paper introduces DCATS, a Data-Centric Agent for Time Series, an LLM-agent framework that improves forecasting by selecting auxiliary time series for fine-tuning rather than by designing a new forecasting architecture.1 In the authors’ traffic forecasting study, GPT-4 Turbo reads metadata about nearby or similar California traffic sensors, proposes candidate neighbour sets, lets lightweight forecasting models test those proposals, and then refines the next round using validation error. ...