To analyze multivariate time series, research through dimension reduction is being conducted, but flexible dimension reduction cannot be achieved by reflecting the characteristics or types of data. This paper proposed a Deep Bidirectional Similarity Learning model (DBSL) that predicts similarities for multivariate time series clustering. This model is a feature extraction-based on Convolutional Neural Networks (CNN). By setting the filter and pooling size according to the size of the data, the convolution operation for attributes and time series and the pooling process for time series are repeated to perform dimension reduction, and the similarities in the time series are predicted through bidirectional Long Short Term Memory (LSTM). To improve the data noise problem for missing values, which is the biggest problem in time series, a simple moving average was applied to the model. In addition, it deals with the overall type, and the model is not specialized for one data type. The experiment was conducted by classifying the data according to whether it was multivariate or missing, and it was confirmed that the performance of the proposed model was higher than other proposed methods.

为了分析多元时间序列，正在通过降维进行研究，但是无法通过反映数据的特征或类型来实现灵活的降维。本文提出了一种深度双向相似性学习模型（DBSL），该模型可预测多元时间序列聚类的相似性。该模型是基于卷积神经网络（CNN）的特征提取。通过根据数据的大小设置过滤器和合并大小，可重复进行属性和时间序列的卷积操作以及时间序列的合并过程以进行维数缩减，并通过双向Long Short预测时间序列的相似性术语记忆（LSTM）。为了改善缺少值的数据噪声问题，这是时间序列中最大的问题，将简单的移动平均线应用于模型。另外，它处理整体类型，并且该模型并不专用于一种数据类型。通过根据数据是多元变量还是缺失数据对数据进行分类来进行实验，证实了该模型的性能高于其他方法。