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.

本文提出了一种通用的MTS预训练模型DBT-DMAE，其中包括缺失表示模块，并给出了一个自编码器结构以获得MTS的编码表示，最后建立整体馈送和丢失策略以确保模型的充分训练。 实验结果表明，DBT-DMAE在6个真实数据集和2个不同的下游任务中均优于其他最先进的方法，并进行了剖析和可解释性实验以验证其有效性。

DBT-DMAE：一种在缺失数据下有效的多变量时间序列预训练模型