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.

翻译：时间序列分析中的缺失数据带来了重大挑战，影响了下游应用的可靠性。填补（即估计缺失值的过程）已成为一种关键解决方案。本文提出了BRATI，一种新颖的深度学习模型，旨在通过结合双向循环网络与注意力机制来解决多元时间序列的填补问题。BRATI通过两个在相反时间方向上运行的填补模块，处理长短期时间跨度的时序依赖与特征关联。每个模块整合了循环层与注意力机制，以有效解决长期依赖问题。我们在三种真实数据集上，针对多种缺失数据场景（随机缺失值、固定长度缺失序列和可变长度缺失序列）评估了BRATI。结果表明，BRATI在多元时间序列数据填补中持续优于现有先进模型，展现出更高的准确性与鲁棒性。