SDA-GRIN：面向自适应时空多元时间序列插补
SDA-GRIN for Adaptive Spatial-Temporal Multivariate Time Series Imputation
AI总结：
本文提出SDA-GRIN，通过多头注意力动态调整图结构并结合循环消息传递，在四个真实数据集上有效提升了多元时间序列缺失值插补精度。
摘要：
在各种应用中，多元时间序列经常存在缺失数据。这一问题会显著干扰依赖这些数据的系统。可以利用空间和时间依赖性来插补缺失样本。现有的插补方法通常忽略空间依赖性的动态变化。我们提出了一种空间动态感知图循环插补网络（SDA-GRIN），它能够捕捉空间依赖性的动态变化。SDA-GRIN利用多头注意力机制使图结构随时间自适应调整。SDA-GRIN将多元时间序列建模为时序图序列，并使用循环消息传递架构进行插补。我们在四个真实世界数据集上评估了SDA-GRIN：在AQI数据集上，SDA-GRIN将MSE改善了9.51%，在AQI-36上改善了9.40%。在PEMS-BAY数据集上，它实现了1.94%的MSE改善。详细的消融研究证明了窗口大小和缺失数据对该方法性能的影响。项目页面：https://ameskandari.github.io/sda-grin/
英文摘要：
In various applications, the multivariate time series often suffers from missing data. This issue can significantly disrupt systems that rely on the data. Spatial and temporal dependencies can be leveraged to impute the missing samples. Existing imputation methods often ignore dynamic changes in spatial dependencies. We propose a Spatial Dynamic Aware Graph Recurrent Imputation Network (SDA-GRIN) which is capable of capturing dynamic changes in spatial dependencies.SDA-GRIN leverages a multi-head attention mechanism to adapt graph structures with time. SDA-GRIN models multivariate time series as a sequence of temporal graphs and uses a recurrent message-passing architecture for imputation. We evaluate SDA-GRIN on four real-world datasets: SDA-GRIN improves MSE by 9.51% for the AQI and 9.40% for AQI-36. On the PEMS-BAY dataset, it achieves a 1.94% improvement in MSE. Detailed ablation study demonstrates the effect of window sizes and missing data on the performance of the method. Project page:https://ameskandari.github.io/sda-grin/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
