Irregularly sampled multivariate time series are ubiquitous in several
application domains, leading to sparse, not fully-observed and non-aligned
observations across different variables. Standard sequential neural network
architectures, such as recurrent neural networks (RNNs) and convolutional
neural networks (CNNs), consider regular spacing between observation times,
posing significant challenges to irregular time series modeling. While most of
the proposed architectures incorporate RNN variants to handle irregular time
intervals, convolutional neural networks have not been adequately studied in
the irregular sampling setting. In this paper, we parameterize convolutional
layers by employing time-explicitly initialized kernels. Such general functions
of time enhance the learning process of continuous-time hidden dynamics and can
be efficiently incorporated into convolutional kernel weights. We, thus,
propose the time-parameterized convolutional neural network (TPCNN), which
shares similar properties with vanilla convolutions but is carefully designed
for irregularly sampled time series. We evaluate TPCNN on both interpolation
and classification tasks involving real-world irregularly sampled multivariate
time series datasets. Our experimental results indicate the competitive
performance of the proposed TPCNN model which is also significantly more
efficient than other state-of-the-art methods. At the same time, the proposed
architecture allows the interpretability of the input series by leveraging the
combination of learnable time functions that improve the network performance in
subsequent tasks and expedite the inaugural application of convolutions in this
field.

不规则采样的多变量时间序列是各种应用领域中普遍存在的现象，本文提出了一种名为时间参数化卷积神经网络（TPCNN）的新型神经网络架构，特别适用于处理不规则时间序列数据，我们对 TPCNN 进行了插值和分类任务的实验评估并与其他最先进方法进行了比较，结果表明 TPCNN 模型不仅具有竞争性的性能，而且比其他方法更高效。同时，该架构利用可学习的时间函数的组合，提高了网络性能和序列输入的可解释性，在该领域中首次应用卷积操作。