TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering

📰 ArXiv cs.AI

TS-Agent enables understanding and reasoning over raw time series data via iterative insight gathering, avoiding representation bottlenecks and improving question answering accuracy

advanced Published 8 Apr 2026

Action Steps

Apply TS-Agent to raw time series data to gather insights iteratively
Avoid converting time series data into LLM-compatible modalities to prevent representation bottlenecks
Use TS-Agent to improve question answering accuracy and reduce hallucination and knowledge leakage
Integrate TS-Agent into existing data analysis pipelines to enhance forecasting and decision-making capabilities

Who Needs to Know This

Data scientists and AI engineers on a team can benefit from TS-Agent as it allows for more accurate time series question answering, while product managers can leverage this technology to improve decision-making and forecasting

Key Insight

💡 TS-Agent overcomes representation bottlenecks in time series question answering by processing raw data directly