用LLM代理增强时间序列预测
Empowering Time Series Forecasting with LLM-Agents
AI总结：
DCATS通过提升数据质量而非模型架构，为时间序列预测的AutoML提供了新的方法，实现了平均6%的误差减少。
摘要：
大型语言模型（LLM）驱动的代理已逐渐成为自动化机器学习（AutoML）系统中的有效规划工具。尽管大多数现有的AutoML方法专注于自动化特征工程和模型架构搜索，但最近的时间序列预测研究表明，轻量级模型往往可以实现最先进的性能。这一观察促使我们探索通过提升数据质量，而非模型架构，作为在时间序列数据上进行AutoML的有潜力的方向。我们提出了DCATS，一个专注于数据的代理用于时间序列。DCATS利用时间序列伴随的元数据来清理数据并优化预测性能。我们使用四个时间序列预测模型在大规模交通流量预测数据集上评估了DCATS。结果表明，DCATS在所有测试模型和时间跨度上实现了平均6%的误差减少，突显了数据导向方法在时间序列预测中的AutoML潜力。
英文摘要：
Large Language Model (LLM) powered agents have emerged as effective planners for Automated Machine Learning (AutoML) systems. While most existing AutoML approaches focus on automating feature engineering and model architecture search, recent studies in time series forecasting suggest that lightweight models can often achieve state-of-the-art performance. This observation led us to explore improving data quality, rather than model architecture, as a potentially fruitful direction for AutoML on time series data. We propose DCATS, a Data-Centric Agent for Time Series. DCATS leverages metadata accompanying time series to clean data while optimizing forecasting performance. We evaluated DCATS using four time series forecasting models on a large-scale traffic volume forecasting dataset. Results demonstrate that DCATS achieves an average 6% error reduction across all tested models and time horizons, highlighting the potential of data-centric approaches in AutoML for time series forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
