论文标题
DBT-DMAE：一个有效的多元时间序列预训练模型在丢失的数据下
DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data
论文作者
论文摘要
多元时间序列（MTS）是与许多实际应用有关的通用数据类型。但是，MTS缺少数据问题，这会导致下游任务（例如预测和分类）的退化甚至崩溃。当遇到多个下游任务时，并发丢失的数据处理过程不可避免地会引起偏见的估计和冗余训练问题。本文提出了普遍适用的MTS预训练模型DBT-DMAE，以征服上述障碍。首先，缺少表示模块是通过引入动态位置嵌入和随机掩盖处理来设计的，以表征缺失的症状。其次，我们提出了一种自动编码器结构，以利用称为动态双向TCN的改善的TCN结构作为基本单元，以获取通用的MTS编码表示，该结构集成了动态内核和时流的技巧来有效地绘制时间特征。最后，建立了整体进食策略，以确保对整个模型进行适当的培训。比较实验结果表明，DBT-DMAE在六个现实世界数据集和两个不同下游任务中的其他最新方法优于其他最新方法。此外，提供消融和可解释性实验，以验证DBT-DMAE子结构的有效性。
Multivariate time series(MTS) is a universal data type related to many practical applications. However, MTS suffers from missing data problems, which leads to degradation or even collapse of the downstream tasks, such as prediction and classification. The concurrent missing data handling procedures could inevitably arouse the biased estimation and redundancy-training problem when encountering multiple downstream tasks. This paper presents a universally applicable MTS pre-train model, DBT-DMAE, to conquer the abovementioned obstacle. First, a missing representation module is designed by introducing dynamic positional embedding and random masking processing to characterize the missing symptom. Second, we proposed an auto-encoder structure to obtain the generalized MTS encoded representation utilizing an ameliorated TCN structure called dynamic-bidirectional-TCN as the basic unit, which integrates the dynamic kernel and time-fliping trick to draw temporal features effectively. Finally, the overall feed-in and loss strategy is established to ensure the adequate training of the whole model. Comparative experiment results manifest that the DBT-DMAE outperforms the other state-of-the-art methods in six real-world datasets and two different downstream tasks. Moreover, ablation and interpretability experiments are delivered to verify the validity of DBT-DMAE's substructures.
