论文标题
使用深度学习发现嘈杂的时间序列数据中的长期依赖关系
Discovering long term dependencies in noisy time series data using deep learning
论文作者
论文摘要
时间序列建模对于解决诸如预测维护，质量控制和优化之类的任务至关重要。深度学习被广泛用于解决此类问题。在通过神经网络管理复杂的制造过程时，工程师需要知道为什么机器学习模型做出了特定的决定以及以下模型建议的可能结果。在本文中，我们开发了使用深神经网络捕获和解释时间序列数据中的时间依赖性的框架，并在各种综合和现实世界数据集上对其进行测试。
Time series modelling is essential for solving tasks such as predictive maintenance, quality control and optimisation. Deep learning is widely used for solving such problems. When managing complex manufacturing process with neural networks, engineers need to know why machine learning model made specific decision and what are possible outcomes of following model recommendation. In this paper we develop framework for capturing and explaining temporal dependencies in time series data using deep neural networks and test it on various synthetic and real world datasets.
