Anomaly detection algorithm of Argo profile based on Sliding Window and ARMA

doi:10.3778/j.issn.1002-8331.1706-0234

Abstract

Abstract: To solve the problem that it is difficult to guarantee the quality of data due to the randomness and abandonment of Argo buoy, according to the Sliding Window（SW） and AutoregRessive Moving Average（ARMA） model, the anomaly detection algorithm of Argo profile based on sliding window and ARMA is proposed. Firstly, the Argo profile series is split by using the sliding window, and then the ARMA model is established so as to get predicted value. Then the confidence interval is determined. Anomaly detection is achieved, finally, by judging whether the observed data is in the confidence interval. The corresponding result is validated with the real Argo profile data, and the sensitivity and accuracy of proposed method reaches at least 85% and 99% respectively when the wide of sliding window between 10 and 20 and the confidence level is in the condition of 80%~90%.

Key words: Argo profile, sliding window, AutoRegressive Moving Average（ARMA）, anomaly detection

摘要： 针对由于Argo浮标的随机性和抛弃性而导致难以保证剖面数据质量的问题，根据滑动窗口（Sliding Window，SW）与自回归移动平均（AutoRegressive Moving Average，ARMA）模型的特点，提出了一种基于滑动窗口和ARMA的Argo剖面异常检测算法。利用滑动窗口将Argo剖面时间序列进行划分，再通过建立ARMA模型获取剖面的预测值，然后确定置信区间，最后通过判断观测数据是否在置信区间内实现异常检测。通过全球Argo浮标剖面数据进行实验，在滑动窗口宽度10~20，置信度在80%~90%时，敏感度可以达到85%以上，且准确度在99%以上。

关键词: Argo浮标, 滑动窗口, 自回归移动平均模型, 异常检测

LUO Yidi, JIANG Hua, WANG Huijiao, WANG Xin. Anomaly detection algorithm of Argo profile based on Sliding Window and ARMA[J]. Computer Engineering and Applications, 2018, 54(19): 254-260.

罗一迪，蒋华，王慧娇，王鑫. 基于滑动窗口和ARMA的Argo剖面数据异常检测算法[J]. 计算机工程与应用, 2018, 54(19): 254-260.

[1]	ZHANG Bowen, LIU Zhi, SANG Guoming. Anomaly Detection Algorithm Based on Kernel Density Fluctuation [J]. Computer Engineering and Applications, 2021, 57(12): 132-136.
[2]	WU Chutian, CHEN Yongle, CHEN Junjie. Cross-Protocol Anomaly Detection Algorithm Based on HMM [J]. Computer Engineering and Applications, 2020, 56(8): 81-86.
[3]	LI Jiayue, ZHAO Bo, LI Xiang, LIU Hui, LIU Yifan, ZOU Jianwen. Network Traffic Anomaly Prediction Method Based on Deep Learning [J]. Computer Engineering and Applications, 2020, 56(6): 39-50.
[4]	HE Huanye, LIN Guoyuan, GU Hao, FANG Menghua. Improved LOF Algorithm in Cloud Virtual Machine Anomaly Detection Scenario [J]. Computer Engineering and Applications, 2020, 56(23): 80-86.
[5]	ZHAN Peng, CHEN Lin, CAO Luhui, XU Haoran, LI Xueqing. Time Series Anomaly Detection Based on Kernel Turning Points Clipped Representation [J]. Computer Engineering and Applications, 2020, 56(23): 130-138.
[6]	XU Jiazhen, LI Ting, YANG Wei. Person Re-Identification by Multi-Scale Local Feature Selection [J]. Computer Engineering and Applications, 2020, 56(2): 141-145.
[7]	LIU Wenfen, MU Xiaodong, HUANG Yuehua. Anomaly Detection Method Based on Multi-resolution Grid [J]. Computer Engineering and Applications, 2020, 56(17): 78-85.
[8]	ZHANG Changhua, ZHOU Xiongtu, ZHANG Yong’ai, YAO Jianmin, GUO Tailiang, YAN Qun. Application Research of Deep Auto Encoder in Data Anomaly Detection [J]. Computer Engineering and Applications, 2020, 56(17): 93-99.
[9]	YANG Yao, FU Kechang, JIANG Tao, ZHANG Guoliang, LIU Jiajia, MENG Yi. AGV Path Planned with Heuristic RRT [J]. Computer Engineering and Applications, 2020, 56(12): 125-133.
[10]	XIONG Yi, LIANG Yiwen, TAN Chengyu, ZHOU Wen. Anomaly Detection Method of Earthquake Precursor Observation Data Based on Negative Selection Algorithm [J]. Computer Engineering and Applications, 2020, 56(10): 226-230.
[11]	CUI Fangyi1, JING Xiaoyuan2, DONG Xiwei2，3, WU Fei2, SUN Ying2. Fuzzy Clustering Based on Adaptive Bat Algorithm Optimization and Its Application [J]. Computer Engineering and Applications, 2019, 55(7): 16-22.
[12]	YANG Feiyue, TAO Yang. Node Anomaly Detection Algorithm Based on Spatial-Temporal Cooperation in WSNs [J]. Computer Engineering and Applications, 2019, 55(7): 127-131.
[13]	WANG Kai, CHEN Danwei. Research on Algorithm of Dynamic Graph Anomaly Detection Based on LSTM [J]. Computer Engineering and Applications, 2019, 55(5): 76-82.
[14]	HE Liang1, WANG Yongcheng1, LI Yun1, CHU Yanjie1, SHEN Chao2. Network Anomaly Detection Algorithm Based on Lindeberg-Feller Central Limit Theorem [J]. Computer Engineering and Applications, 2019, 55(4): 41-47.
[15]	WU Jingfeng, JIN Weidong, TANG Peng. Catenary Pillar Image Anomaly Detection Combined with SVDD and CNN [J]. Computer Engineering and Applications, 2019, 55(10): 193-198.

Anomaly detection algorithm of Argo profile based on Sliding Window and ARMA

基于滑动窗口和ARMA的Argo剖面数据异常检测算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics