scholarly journals Survey and Proposal of an Adaptive Anomaly Detection Algorithm for Periodic Data Streams

2019 ◽  
Vol 07 (08) ◽  
pp. 33-55
Author(s):  
Zirije Hasani ◽  
Samedin Krrabaj
Keyword(s):  
Anomaly Detection ◽  
Data Streams ◽  
Detection Algorithm ◽  
Periodic Data

scholarly journals Research on the Fastest Detection Method for Weak Trends under Noise Interference

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1093
Author(s):  
Guang Li ◽  
Jing Liang ◽  
Caitong Yue
Keyword(s):  
Anomaly Detection ◽  
Data Streams ◽  
Concept Drift ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Industrial Data ◽  
Oil Drilling ◽  
Noise Interference ◽  
Detection Score ◽  
Weak Trend

Trend anomaly detection is the practice of comparing and analyzing current and historical data trends to detect real-time abnormalities in online industrial data-streams. It has the advantages of tracking a concept drift automatically and predicting trend changes in the shortest time, making it important both for algorithmic research and industry. However, industrial data streams contain considerable noise that interferes with detecting weak anomalies. In this paper, the fastest detection algorithm “sliding nesting” is adopted. It is based on calculating the data weight in each window by applying variable weights, while maintaining the method of trend-effective integration accumulation. The new algorithm changes the traditional calculation method of the trend anomaly detection score, which calculates the score in a short window. This algorithm, SNWFD–DS, can detect weak trend abnormalities in the presence of noise interference. Compared with other methods, it has significant advantages. An on-site oil drilling data test shows that this method can significantly reduce delays compared with other methods and can improve the detection accuracy of weak trend anomalies under noise interference.

A Fast kNN-Based Approach for Time Sensitive Anomaly Detection over Data Streams

2019 ◽  
pp. 59-74 ◽  
Author(s):  
Guangjun Wu ◽  
Zhihui Zhao ◽  
Ge Fu ◽  
Haiping Wang ◽  
Yong Wang ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Data Streams
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