论文标题
在多元不规则采样时间序列中预测，值缺失
Forecasting in multivariate irregularly sampled time series with missing values
论文作者
论文摘要
稀疏和不规则采样的多元时间序列在临床，气候，财务和许多其他领域中很常见。最近的方法集中在此类数据上的分类，回归或预测任务上。在预测中，不仅有必要预测正确的值，而且还必须在不规则时间序列中发生该值时进行预测。在这项工作中，我们提出了一种预测值的方法，不仅是预期的时间，还提出了预期的时间。
Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains. Most recent approaches focus on classification, regression or forecasting tasks on such data. In forecasting, it is necessary to not only forecast the right value but also to forecast when that value will occur in the irregular time series. In this work, we present an approach to forecast not only the values but also the time at which they are expected to occur.
