论文标题
通过密集的时间描述符改善不规则抽样的时间序列学习
Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time
论文作者
论文摘要
由于处理不规则的时间间隔的障碍，对不规则采样时间序列的监督学习是对机器学习方法的挑战。一些论文介绍了最近涉及不规则性的最近经常发生的神经网络模型，但其中大多数依靠复杂的机制来取得更好的性能。这项工作提出了一种新的方法，可以使用正弦函数（称为时间嵌入）表示时间戳（小时或日期）作为密集的向量。作为数据输入方法，可以应用于大多数机器学习模型。该方法通过模拟III的两个预测任务进行评估，该任务是电子健康记录的不规则抽样时间序列的数据集。我们的测试显示了基于LSTM和经典的机器学习模型的改进，特别是数据非常不规则的数据。
Supervised learning with irregularly sampled time series have been a challenge to Machine Learning methods due to the obstacle of dealing with irregular time intervals. Some papers introduced recently recurrent neural network models that deals with irregularity, but most of them rely on complex mechanisms to achieve a better performance. This work propose a novel method to represent timestamps (hours or dates) as dense vectors using sinusoidal functions, called Time Embeddings. As a data input method it and can be applied to most machine learning models. The method was evaluated with two predictive tasks from MIMIC III, a dataset of irregularly sampled time series of electronic health records. Our tests showed an improvement to LSTM-based and classical machine learning models, specially with very irregular data.
