Missing data in time-series analysis poses significant challenges, affecting the reliability of downstream applications. Imputation, the process of estimating missing values, has emerged as a key solution. This paper introduces BRATI, a novel deep-learning model designed to address multivariate time-series imputation by combining Bidirectional Recurrent Networks and Attention mechanisms. BRATI processes temporal dependencies and feature correlations across long and short time horizons, utilizing two imputation blocks that operate in opposite temporal directions. Each block integrates recurrent layers and attention mechanisms to effectively resolve long-term dependencies. We evaluate BRATI on three real-world datasets under diverse missing-data scenarios: randomly missing values, fixed-length missing sequences, and variable-length missing sequences. Our findings demonstrate that BRATI consistently outperforms state-of-the-art models, delivering superior accuracy and robustness in imputing multivariate time-series data.

本研究针对时间序列分析中缺失数据的问题，提出了一种新颖的深度学习模型BRATI，用于多变量时间序列插补。该模型结合了双向递归网络和注意力机制，有效处理时间依赖性和特征关联，实验结果表明，BRATI在各种缺失数据场景下均优于现有最先进模型，具有更高的准确性和鲁棒性。

BRATI：双向递归注意力用于时间序列插补