论文标题
监视时间序列缺失值：一种深层概率方法
Monitoring Time Series With Missing Values: a Deep Probabilistic Approach
论文作者
论文摘要
通常通过收集和流式传输多元时间序列来监视系统以确保健康和安全。由于采用多层复发性神经网络体系结构而导致的时间序列预测的进步使得可以根据趋势的细微变化来预测高维时间序列，并尽早确定和分类新颖性。但是，当持续的预测必须包括不确定性，对于丢失数据，主流时间序列预测的主流方法无法处理良好的情况。我们基于在高维时间序列中预测的最先进方法的结合，对时间序列监测介绍了新的体系结构，并提供了完全概率处理不确定性的方法。我们展示了时间序列预测和新颖性检测的架构的优势，尤其是部分缺少数据，并经验评估和将体系结构与现实世界数据集的最新方法进行比较。
Systems are commonly monitored for health and security through collection and streaming of multivariate time series. Advances in time series forecasting due to adoption of multilayer recurrent neural network architectures make it possible to forecast in high-dimensional time series, and identify and classify novelties early, based on subtle changes in the trends. However, mainstream approaches to multi-variate time series predictions do not handle well cases when the ongoing forecast must include uncertainty, nor they are robust to missing data. We introduce a new architecture for time series monitoring based on combination of state-of-the-art methods of forecasting in high-dimensional time series with full probabilistic handling of uncertainty. We demonstrate advantage of the architecture for time series forecasting and novelty detection, in particular with partially missing data, and empirically evaluate and compare the architecture to state-of-the-art approaches on a real-world data set.
