Prevalent in many real-world settings such as healthcare, irregular time series are challenging to formulate predictions from. It is difficult to infer the value of a feature at any given time when observations are sporadic, as it could take on a range of values depending on when it was last observed. To characterize this uncertainty we present EDICT, a strategy that learns an evidential distribution over irregular time series in continuous time. This distribution enables well-calibrated and flexible inference of partially observed features at any time of interest, while expanding uncertainty temporally for sparse, irregular observations. We demonstrate that EDICT attains competitive performance on challenging time series classification tasks and enabling uncertainty-guided inference when encountering noisy data.

翻译：在许多真实场景（如医疗保健）中普遍存在的不规则时间序列，对其进行预测具有挑战性。当观测数据稀疏时，难以推断任意时刻的特征值，因为该值可能根据上次观测时间的不同而呈现多种可能性。为表征这种不确定性，我们提出EDICT策略，该策略在连续时间框架下学习不规则时间序列的证据分布。该分布能够在任意感兴趣时刻对部分观测特征进行校准良好且灵活可调的推断，同时针对稀疏的不规则观测在时间维度上扩展不确定性。实验表明，EDICT在具有挑战性的时间序列分类任务中取得了竞争性表现，并在处理噪声数据时实现了不确定性引导的推断。