具有缺失值的时间序列监测:一种深度概率方法

Oshri Barazani, David Tolpin
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引用次数: 0

摘要

通常通过多变量时间序列的收集和流来监控系统的健康和安全。由于采用多层递归神经网络架构,时间序列预测的进步使得在高维时间序列中进行预测成为可能,并根据趋势的细微变化及早识别和分类新奇事物。然而,多变量时间序列预测的主流方法不能很好地处理正在进行的预测必须包含不确定性的情况,它们对缺失数据的鲁棒性也不强。我们介绍了一种新的时间序列监测体系结构,该体系结构结合了高维时间序列预测的最新方法和对不确定性的全概率处理。我们展示了该体系结构在时间序列预测和新颖性检测方面的优势,特别是在部分缺失数据的情况下,并对该体系结构与现实世界数据集上的最新方法进行了经验评估和比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring Time Series With Missing Values: a Deep Probabilistic Approach
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.
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