Li Yurui, Du Mingjing, He Sheng
School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, China.
Entropy (Basel). 2022 Dec 9;24(12):1798. doi: 10.3390/e24121798.
Time series data are usually characterized by having missing values, high dimensionality, and large data volume. To solve the problem of high-dimensional time series with missing values, this paper proposes an attention-based sequence-to-sequence model to imputation missing values in time series (ASSM), which is a sequence-to-sequence model based on the combination of feature learning and data computation. The model consists of two parts, encoder and decoder. The encoder part is a BIGRU recurrent neural network and incorporates a self-attentive mechanism to make the model more capable of handling long-range time series; The decoder part is a GRU recurrent neural network and incorporates a cross-attentive mechanism into associate with the encoder part. The relationship weights between the generated sequences in the decoder part and the known sequences in the encoder part are calculated to achieve the purpose of focusing on the sequences with a high degree of correlation. In this paper, we conduct comparison experiments with four evaluation metrics and six models on four real datasets. The experimental results show that the model proposed in this paper outperforms the six comparative missing value interpolation algorithms.
时间序列数据通常具有缺失值、高维度和大数据量的特点。为了解决具有缺失值的高维时间序列问题，本文提出了一种基于注意力的序列到序列模型来插补时间序列中的缺失值（ASSM），这是一种基于特征学习和数据计算相结合的序列到序列模型。该模型由两部分组成，编码器和解码器。编码器部分是一个双向门控循环单元（BIGRU）递归神经网络，并结合了自注意力机制，使模型更有能力处理长程时间序列；解码器部分是一个门控循环单元（GRU）递归神经网络，并结合了交叉注意力机制与编码器部分相关联。计算解码器部分生成的序列与编码器部分已知序列之间的关系权重，以实现关注高度相关序列的目的。在本文中，我们在四个真实数据集上使用四个评估指标和六个模型进行了比较实验。实验结果表明，本文提出的模型优于六种比较缺失值插值算法。
