Title: Monitoring Time Series With Missing Values: a Deep Probabilistic Approach Show Chinese title

Title: 带有缺失值的时间序列监测：一种深度概率方法

Abstract: 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. Show Chinese abstract

Abstract: 系统通常通过收集和流式传输多变量时间序列来监控健康和安全。 由于采用多层循环神经网络架构，时间序列预测取得了进展，使得能够在高维时间序列中进行预测，并基于趋势的细微变化早期识别和分类异常情况。 然而，主流的多变量时间序列预测方法在处理正在进行的预测必须包含不确定性的情况时表现不佳，并且对缺失数据不够鲁棒。 我们引入了一种新的时间序列监控架构，该架构结合了高维时间序列预测的最先进方法与不确定性的完整概率处理。 我们展示了该架构在时间序列预测和异常检测中的优势，特别是在部分数据缺失的情况下，并在真实世界的数据集上对架构进行了实证评估和比较。