论文标题
学会使用极度不平衡的数据来预测垂直轨道不规则性
Learn to Predict Vertical Track Irregularity with Extremely Imbalanced Data
论文作者
论文摘要
铁路系统需要定期的手动维护，其中很大一部分致力于检查轨道变形。这种变形可能会严重影响火车的运行时安全性，而这种检查对于金融和人力资源来说仍然是昂贵的。因此，迫切需要一种更精确，更有效的检测铁路轨道变形的方法。在本文中，我们展示了一个用于预测垂直轨道不规则性的应用程序框架，该框架基于中国几家运营铁路生产的现实世界中的大规模数据集。我们已经对各种机器学习和集合学习算法进行了广泛的实验，以最大程度地提高模型捕获任何不规则性的能力。我们还提出了一种新的方法，用于处理多元时间序列预测任务中的不平衡数据，并使用自适应数据采样和受到惩罚损失。事实证明，这种方法可以降低模型对目标域不平衡的敏感性，从而提高其在预测罕见极端值方面的性能。
Railway systems require regular manual maintenance, a large part of which is dedicated to inspecting track deformation. Such deformation might severely impact trains' runtime security, whereas such inspections remain costly for both finance and human resources. Therefore, a more precise and efficient approach to detect railway track deformation is in urgent need. In this paper, we showcase an application framework for predicting vertical track irregularity, based on a real-world, large-scale dataset produced by several operating railways in China. We have conducted extensive experiments on various machine learning & ensemble learning algorithms in an effort to maximize the model's capability in capturing any irregularity. We also proposed a novel approach for handling imbalanced data in multivariate time series prediction tasks with adaptive data sampling and penalized loss. Such an approach has proven to reduce models' sensitivity to the imbalanced target domain, thus improving its performance in predicting rare extreme values.
