计算机科学
聚类分析
时间序列
异常检测
无监督学习
系列(地层学)
特征学习
人工智能
数据挖掘
深度学习
维数之咒
物联网
机器学习
特征提取
万维网
古生物学
生物
作者
Ya Liu,Yingjie Zhou,Kai Yang,Xin Wang
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-08-15
卷期号:10 (16): 14285-14306
被引量:4
标识
DOI:10.1109/jiot.2023.3243391
摘要
Internet of Things (IoT) time-series analysis has found numerous applications in a wide variety of areas, ranging from health informatics to network security. Nevertheless, the complex spatial–temporal dynamics and high dimensionality of IoT time series make the analysis increasingly challenging. In recent years, the powerful feature extraction and representation learning capabilities of deep learning (DL) have provided an effective means for IoT time-series analysis. However, few existing surveys on time series have systematically discussed unsupervised DL-based methods. To fill this void, we investigate unsupervised DL for IoT time series, i.e., unsupervised anomaly detection and clustering, under a unified framework. We also discuss the application scenarios, public data sets, existing challenges, and future research directions in this area.
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