论文标题
使用样条网络进行不规则采样的时间序列建模
Irregularly-Sampled Time Series Modeling with Spline Networks
论文作者
论文摘要
连续时间进行的观察通常是不规则的，并且包含跨不同通道的缺失值。处理丢失数据的一种方法是使用花键将其拟合，将分段多项式拟合到观察到的值。我们建议将花朵作为对神经网络的输入，特别是直接应用插值函数上的转换，而不是对网格上的点进行采样。为此，我们设计了可以在花纹上运行并且类似于其离散对应物的层。这使我们能够紧凑地表示不规则的序列，并在下游任务（例如分类和预测）中使用此表示。与现有方法相比，我们的模型在准确性和计算效率方面提供了竞争性能。
Observations made in continuous time are often irregular and contain the missing values across different channels. One approach to handle the missing data is imputing it using splines, by fitting the piecewise polynomials to the observed values. We propose using the splines as an input to a neural network, in particular, applying the transformations on the interpolating function directly, instead of sampling the points on a grid. To do that, we design the layers that can operate on splines and which are analogous to their discrete counterparts. This allows us to represent the irregular sequence compactly and use this representation in the downstream tasks such as classification and forecasting. Our model offers competitive performance compared to the existing methods both in terms of the accuracy and computation efficiency.
