论文标题
从不规则采样的时间序列中学习：缺少的数据角度
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective
论文作者
论文摘要
不规则采样的时间序列发生在包括医疗保健在内的许多领域。他们可能会具有挑战性的模型，因为它们不会自然地产生许多标准机器学习模型所要求的固定维表示。在本文中，我们考虑从丢失数据的角度考虑不规则采样。我们将观察到的不规则采样的时间序列数据模拟为从连续但未观察到的函数采样的索引值对序列。我们引入了一个编码器框架，用于从此类通用索引序列中学习。我们建议基于变异自动编码器和生成对抗网络的学习方法。对于连续不规则采样的时间序列，我们引入了连续的卷积层，可以有效地与现有的神经网络体系结构接口。实验表明，与最近的RNN模型相比，我们的模型能够在不规则采样的多元时间序列上获得竞争性或更好的分类结果，同时提供更快的训练时间。
Irregularly-sampled time series occur in many domains including healthcare. They can be challenging to model because they do not naturally yield a fixed-dimensional representation as required by many standard machine learning models. In this paper, we consider irregular sampling from the perspective of missing data. We model observed irregularly-sampled time series data as a sequence of index-value pairs sampled from a continuous but unobserved function. We introduce an encoder-decoder framework for learning from such generic indexed sequences. We propose learning methods for this framework based on variational autoencoders and generative adversarial networks. For continuous irregularly-sampled time series, we introduce continuous convolutional layers that can efficiently interface with existing neural network architectures. Experiments show that our models are able to achieve competitive or better classification results on irregularly-sampled multivariate time series compared to recent RNN models while offering significantly faster training times.
