Abstract
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.
Abstract (translated)
大型语言模型（LLM）驱动的代理已作为自动化机器学习（AutoML）系统中的有效规划者出现。尽管现有的大多数AutoML方法主要关注自动化的特征工程和模型架构搜索，但近期在时间序列预测领域的研究表明，轻量级模型往往可以达到最先进的性能水平。这一观察结果促使我们探索提升数据质量而非改进模型架构作为改善时间序列数据上的AutoML的一种潜在有效方向。为此，我们提出了DCATS（面向时间序列的数据中心代理）。DCATS利用伴随时间序列的元数据来清理数据并优化预测性能。 我们在一个大规模的交通流量预测数据集上使用四种时间序列预测模型对DCATS进行了评估。结果表明，与所有测试过的模型和时间范围相比，DCATS实现了平均6%的误差减少，这突显了在时间序列预测中采用数据为中心的方法对于AutoML的巨大潜力。
URL
https://arxiv.org/abs/2508.04231
