论文标题
基于自动编码器的时间序列聚类与能源应用
Autoencoder-based time series clustering with energy applications
论文作者
论文摘要
由于数据的特定性质，时间序列聚类是一项具有挑战性的任务。经典方法表现不佳，需要通过新的距离度量或数据转换来调整。在本文中，我们研究了卷积自动编码器和K-Medoids算法与完善时间序列聚类的组合。卷积自动编码器允许提取有意义的功能并降低数据的尺寸，从而改善了随后的聚类。使用仿真和与能量相关的数据来验证该方法，实验结果表明，聚类对离群值的鲁棒性与标准方法相比，导致群集的群集更强。
Time series clustering is a challenging task due to the specific nature of the data. Classical approaches do not perform well and need to be adapted either through a new distance measure or a data transformation. In this paper we investigate the combination of a convolutional autoencoder and a k-medoids algorithm to perfom time series clustering. The convolutional autoencoder allows to extract meaningful features and reduce the dimension of the data, leading to an improvement of the subsequent clustering. Using simulation and energy related data to validate the approach, experimental results show that the clustering is robust to outliers thus leading to finer clusters than with standard methods.
