Time series data are an extremely important type of data in the real world. Time series data gradually accumulate over time. Due to the dynamic growth in time series data, they tend to have higher dimensions and large data scales. When performing cluster analysis on this type of data, there are shortcomings in using traditional feature extraction methods for processing. To improve the clustering performance on time series data, this study uses a recurrent neural network (RNN) to train the input data. First, an RNN called the long short-term memory (LSTM) network is used to extract the features of time series data. Second, pooling technology is used to reduce the dimensionality of the output features in the last layer of the LSTM network. Due to the long time series, the hidden layer in the LSTM network cannot remember the information at all times. As a result, it is difficult to obtain a compressed representation of the global information in the last layer. Therefore, it is necessary to combine the information from the previous hidden unit to supplement all of the data. By stacking all the hidden unit information and performing a pooling operation, a dimensionality reduction effect of the hidden unit information is achieved. In this way, the memory loss caused by an excessively long sequence is compensated. Finally, considering that many time series data are unbalanced data, the unbalanced K-means (UK-means) algorithm is used to cluster the features after dimensionality reduction. The experiments were conducted on multiple publicly available time series datasets. The experimental results show that LSTM-based feature extraction combined with the dimensionality reduction processing of the pooling technology and cluster processing for imbalanced data used in this study has a good effect on the processing of time series data.

中文翻译：

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具有时间序列数据分析功能的新型长期和短期存储网络

时间序列数据是现实世界中极为重要的数据类型。时间序列数据会随着时间逐渐积累。由于时间序列数据的动态增长，它们往往具有更高的维度和更大的数据规模。对此类数据执行聚类分析时，使用传统特征提取方法进行处理存在缺陷。为了提高时间序列数据的聚类性能，本研究使用递归神经网络（RNN）训练输入数据。首先，称为长短期记忆（LSTM）网络的RNN用于提取时间序列数据的特征。其次，池化技术用于降低LSTM网络最后一层中输出特征的维数。由于时间序列较长，因此LSTM网络中的隐藏层无法始终记住信息。结果，难以获得最后一层中全局信息的压缩表示。因此，有必要组合来自先前隐藏单元的信息以补充所有数据。通过堆叠所有隐藏单元信息并执行合并操作，可以实现隐藏单元信息的降维效果。这样，可以补偿由于序列过长而导致的存储丢失。最后，考虑到许多时间序列数据是不平衡数据，从而实现了隐藏单元信息的降维效果。这样，可以补偿由于序列过长而导致的存储丢失。最后，考虑到许多时间序列数据是不平衡数据，从而实现了隐藏单元信息的降维效果。这样，可以补偿由于序列过长而导致的存储丢失。最后，考虑到许多时间序列数据是不平衡数据，K均值（UK -means）算法用于在降维后对特征进行聚类。实验是在多个公开的时间序列数据集上进行的。实验结果表明，基于LSTM的特征提取与池化技术的降维处理以及不平衡数据的聚类处理相结合，对时间序列数据的处理具有良好的效果。