论文标题
SAIT：基于自我注意力的时间序列的插定
SAITS: Self-Attention-based Imputation for Time Series
论文作者
论文摘要
时间序列中丢失的数据是一个普遍的问题，它使障碍物陷入了高级分析的方式。一个流行的解决方案是插补，基本挑战是确定应该填写哪些值。本文提出了一种基于多元时间序列中缺少价值插补的自我发挥作用机制的新颖方法。通过一种联合优化方法训练，SAIT从两个对角线掩盖的自我注意力（DMSA）块的加权组合中学习了缺失的值。 DMSA明确捕获时间步骤之间的时间依赖性和特征相关性，从而提高了插补的准确性和训练速度。同时，加权组合设计使SAIT可以根据注意图和缺失信息从两个DMSA块中动态分配权重。广泛的实验定量和定性地表明，SAIT的表现优于时间序列的最先进方法，并有效地推出任务，并揭示了SAIT的潜力，可以在现实世界中不完整的时间序列数据上提高模式识别模型的学习性能。该代码是https://github.com/wenjiedu/saits上的GitHub上的开源。
Missing data in time series is a pervasive problem that puts obstacles in the way of advanced analysis. A popular solution is imputation, where the fundamental challenge is to determine what values should be filled in. This paper proposes SAITS, a novel method based on the self-attention mechanism for missing value imputation in multivariate time series. Trained by a joint-optimization approach, SAITS learns missing values from a weighted combination of two diagonally-masked self-attention (DMSA) blocks. DMSA explicitly captures both the temporal dependencies and feature correlations between time steps, which improves imputation accuracy and training speed. Meanwhile, the weighted-combination design enables SAITS to dynamically assign weights to the learned representations from two DMSA blocks according to the attention map and the missingness information. Extensive experiments quantitatively and qualitatively demonstrate that SAITS outperforms the state-of-the-art methods on the time-series imputation task efficiently and reveal SAITS' potential to improve the learning performance of pattern recognition models on incomplete time-series data from the real world. The code is open source on GitHub at https://github.com/WenjieDu/SAITS.
