Many real-world applications involve multivariate, geo-tagged time series
data: at each location, multiple sensors record corresponding measurements. For
example, air quality monitoring system records PM2.5, CO, etc. The resulting
time-series data often has missing values due to device outages or
communication errors. In order to impute the missing values, state-of-the-art
methods are built on Recurrent Neural Networks (RNN), which process each time
stamp sequentially, prohibiting the direct modeling of the relationship between
distant time stamps. Recently, the self-attention mechanism has been proposed
for sequence modeling tasks such as machine translation, significantly
outperforming RNN because the relationship between each two time stamps can be
modeled explicitly. In this paper, we are the first to adapt the self-attention
mechanism for multivariate, geo-tagged time series data. In order to jointly
capture the self-attention across multiple dimensions, including time, location
and the sensor measurements, while maintain low computational complexity, we
propose a novel approach called Cross-Dimensional Self-Attention (CDSA) to
process each dimension sequentially, yet in an order-independent manner. Our
extensive experiments on four real-world datasets, including three standard
benchmarks and our newly collected NYC-traffic dataset, demonstrate that our
approach outperforms the state-of-the-art imputation and forecasting methods. A
detailed systematic analysis confirms the effectiveness of our design choices.

本论文利用自我关注机制来处理多元、地理标记的时间序列数据中的缺失值，并通过跨维度自我关注 (CDSA) 的新方法，提出了一种具有低计算复杂度的序列建模算法，超越了现有的标准预测和补全方法。