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.

中文翻译：

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具有缺失值的多元不规则采样时间序列的预测

稀疏和不规则采样的多元时间序列在临床、气候、金融和许多其他领域很常见。最近的方法侧重于对此类数据进行分类、回归或预测任务。在预测中，不仅要预测正确的值，还要预测该值何时会出现在不规则的时间序列中。在这项工作中，我们提出了一种方法，不仅可以预测值，还可以预测它们预计发生的时间。