论文标题
弹性神经预测系统
Resilient Neural Forecasting Systems
论文作者
论文摘要
工业机器学习系统面临的数据挑战通常在学术文献中探讨。常见的数据挑战是数据分布变化，缺失值和异常。在本文中，我们在劳动计划上的神经预测应用程序中讨论了数据挑战和解决方案。我们讨论如何使这种预测系统应对这些数据挑战的弹性。我们使用定期重新培训方案解决数据分布的变化，并讨论在这种情况下模型稳定性的重要重要性。此外，我们展示了我们的深度学习模型如何本地处理丢失的价值，而无需归类。最后，我们描述了如何检测输入数据中的异常情况并在影响预测之前减轻效果。这导致了一个完全自主的预测系统，该系统与由算法和人类覆盖的混合系统进行了比较。
Industrial machine learning systems face data challenges that are often under-explored in the academic literature. Common data challenges are data distribution shifts, missing values and anomalies. In this paper, we discuss data challenges and solutions in the context of a Neural Forecasting application on labor planning.We discuss how to make this forecasting system resilient to these data challenges. We address changes in data distribution with a periodic retraining scheme and discuss the critical importance of model stability in this setting. Furthermore, we show how our deep learning model deals with missing values natively without requiring imputation. Finally, we describe how we detect anomalies in the input data and mitigate their effect before they impact the forecasts. This results in a fully autonomous forecasting system that compares favorably to a hybrid system consisting of the algorithm and human overrides.
