气候变化领域集成服务门户(CCPortal): An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data

CCPortal

DOI	10.1109/ACCESS.2019.2957602
	An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data
	Zhang, Minghu; Li, Xin; Wang, Lili
通讯作者	Li, X (通讯作者)
发表日期	2019
ISSN	2169-3536
起始页码	175192
结束页码	175212
卷号	7
英文摘要	The intelligent environment monitoring network, as the foundation of ecosystem research, has rapidly developed with the ever-growing Internet of Things (IoT). IoT-networked sensors deployed to monitor ecosystems generate copious sensor data characterized by nonstationarity and nonlinearity such that outlier detection remains a source of concern. Most outlier detection models involve hypothesis tests based on setting outlier threshold values. However, signal decomposition describes stationary and nonstationary relationships sensor data. Therefore, this paper proposes a three-level hybrid model based on the median filter (MF), empirical mode decomposition (EMD), classification and regression tree (CART), autoregression (AR) and exponential weighted moving average (EWMA) methods called MF-EMD-CART-AR-EWMA to detect outliers in sensor data. The first-level performance is compared to that of the Butterworth filter, FIR filter, moving average filter, wavelet filter and Wiener filter. The second-level prediction performance is compared to support vector regression (SVR), K-nearest neighbor (KNN), CART, complementary ensemble EEMD with CART and AR (EEMD-CART-AR) and ensemble CEEMD with CART and AR (CEEMD-CART-AR) methods. Finally, EWMA is compared to Cumulative Sum Control Chart (CUSUM) and Shewhart control charts. The proposed hybrid model was evaluated with a real dataset from the hydrometeorological observation network in the Heihe River Basin, yielding experimental results with better generalization ability and higher accuracy than the compared models, and providing extremely effective detection of minor outliers in predicted values. This paper provides valuable insight and a promising reference for outlier detection involving sensor data and presents a new perspective for detecting outliers.
关键词	EMPIRICAL MODE DECOMPOSITION INTERNET REGRESSION TRANSFORM THINGS
英文关键词	Environmental monitoring; sensor data; outlier detection; integrated model; statistical analysis
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000509399500001
来源期刊	IEEE ACCESS
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259508
推荐引用方式 GB/T 7714	Zhang, Minghu,Li, Xin,Wang, Lili. An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data[J]. 中国科学院青藏高原研究所,2019,7.
APA	Zhang, Minghu,Li, Xin,&Wang, Lili.(2019).An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data.IEEE ACCESS,7.
MLA	Zhang, Minghu,et al."An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data".IEEE ACCESS 7(2019).