自动优化时间序列输入长度与采样率，提升预测精度。
ATLO-ML: Adaptive Time-Length Optimizer for Machine Learning -- Insights from Air Quality Forecasting
- 根据输出时长自适应调整输入时长和采样率
- 在空气质量数据上显著优于固定时长设置
- 适合需优化时间参数的时序建模场景
机器学习中的时间序列预测精度高度依赖于输入时长和采样率的选择。本文提出ATLO-ML，一种自适应时间长度优化系统，可根据用户定义的输出时长自动确定最优输入时长和采样率。该系统为时间序列预处理提供灵活方案，动态调整参数以提升预测性能。在包含GAMS数据集和数据中心采集的专有数据集的空气质量数据上进行了验证，结果表明，采用优化后的时长与采样率能显著提高模型准确率。该方法具备跨多种时敏应用的泛化潜力，为机器学习流程中的时间输入参数优化提供了稳健解决方案。
原文摘要 · Abstract (English)
Accurate time-series predictions in machine learning are heavily influenced by the selection of appropriate input time length and sampling rate. This paper introduces ATLO-ML, an adaptive time-length optimization system that automatically determines the optimal input time length and sampling rate based on user-defined output time length. The system provides a flexible approach to time-series data pre-processing, dynamically adjusting these parameters to enhance predictive performance. ATLO-ML is validated using air quality datasets, including both GAMS-dataset and proprietary data collected from a data center, both in time series format. Results demonstrate that utilizing the optimized time length and sampling rate significantly improves the accuracy of machine learning models compared to fixed time lengths. ATLO-ML shows potential for generalization across various time-sensitive applications, offering a robust solution for optimizing temporal input parameters in machine learning workflows.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。