A hybrid algorithm for mining local outliers in categorical data

Liu, Meiling; Huang, Mingxuan; Tang, Weidong

doi:10.1504/ijwmc.2017.087342

Cited by 1 publication

(2 citation statements)

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“…Both point anomaly and pattern anomaly are abnormal behaviors appeared in an individual time series, while sequence anomaly is abnormal behavior appeared between sequences. Existing models for anomaly detection of time series are based on statistics [26,27], distance [28,29], machine learning [30,31], and artificial intelligence [32,33].…”

Section: Three Kinds Of Anomaly In Time Seriesmentioning

confidence: 99%

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An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

Gao

Yang

Chen

et al. 2021

Complex Intell. Syst.

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With large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

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Section: Three Kinds Of Anomaly In Time Seriesmentioning

confidence: 99%

“…For t ∈ [1201, 2190], we introduce eight abnormal patterns and three distracters. Specifically, the eight abnormal patterns are e a (t) = i+1 20 • sin 40π K t , t ∈ [1200+60(i −1), 1230+60(i −1)] , 0, otherwise , (28) where i = 1, 2, . .…”

Section: Real-world Data Setsmentioning

confidence: 99%