双向注意力网络提升时间序列缺失值填补准确率
BRATI: Bidirectional Recurrent Attention for Time-Series Imputation
- 用双向循环网络+注意力机制捕捉长短时序依赖
- 在三种缺失场景下均优于现有模型，最高提升12.3%
- 适合处理医疗、金融等多变量时序数据缺失问题
时间序列中的缺失数据严重影响下游应用的可靠性。填补缺失值是关键解决方案。本文提出BRATI，一种结合双向循环网络与注意力机制的深度学习模型，用于多变量时间序列填补。BRATI通过两个反向运行的填补模块，分别处理长短期时间依赖和特征相关性。我们在三个真实世界数据集上评估了BRATI在不同缺失模式下的表现：随机缺失、固定长度缺失序列、可变长度缺失序列。结果表明，BRATI在所有场景中均持续优于现有先进模型，在多个指标上实现显著提升，最高达12.3%。该方法具有更强的准确性和鲁棒性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。