KPI Data Anomaly Detection Strategy for Intelligent Operation and Maintenance Under Cloud Environment

Xu, Youchang; Chen, Ningjiang; Huang, Ruwei; Zhang, Hanlin

doi:10.1007/978-3-030-00828-4_31

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Time series analysis and mining have attracted attention in recent years [7]. Time series applications have been widely used in transportation [8], operation and maintenance [9][10][11], and medical [12][13][14][15] fields. At present, the development status of time series anomaly detection algorithm can be roughly divided into three stages.…”

Section: Related Workmentioning

confidence: 99%

Time Series Anomaly Detection Model Based on Multi-Features

Tang

Wang

Jiang

2022

Computational Intelligence and Neuroscience

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For Internet information services, it is very important to closely monitor a large number of key time series data generated by core business for anomaly detection. Although there have been many anomaly detection models in recent years, its practical application is still a big challenge. The model usually needs repeated iteration and parameter adjustment; and for different types of time series data, we need to select different models. Therefore, this paper proposes an anomaly detection model based on time series. The model first designs the statistical features, fitting features, and time-frequency domain features for the time series, and then uses the random forest integration model to automatically select the appropriate features for anomaly classification. In addition, this paper presents an anomaly evaluation index ADC score with timeliness window, which adds the time delay factor of anomaly detection on the basis of F1-score. We use the KPI time series, a representative key performance index in the industry, as the experimental data. It is found that the ADC score of the anomaly detection model in this paper reaches the level of 0.7–0.8, which can meet the needs of practical application.

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Section: Related Workmentioning

confidence: 99%

Time Series Anomaly Detection Model Based on Multi-Features

Tang

Wang

Jiang

2022

Computational Intelligence and Neuroscience

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“…References Time series analysis [162], [161], [76], [198], [83], [71], [210], [70] Bayesian learning [118], [85], [14], [103], [159], [50], [35], [128], [105], [157] Principal component analysis [195], [5], [18], [113], [108] Regression analysis [205], [54], [25], [89], [156] Logistic regression [131], [115], [26], [207] Hidden Markov model [79], [78], [11], [170] Markov chain [167], [52], [41] Gaussian mixture models [106], [13] Statistical tests [173], [114] Restricted Boltzmann machine [110], [ [201], [200], [63], Wavelet transform…”

Section: Methodsmentioning

confidence: 99%

A Systematic Review on Anomaly Detection for Cloud Computing Environments

Hagemann

Katsarou

2020

2020 3rd Artificial Intelligence and Cloud Computing Conference

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The detection of anomalies in data is a far-reaching field of research which also applies to the field of cloud computing in several different ways: from the detection of various types of intrusions to the detection of hardware failures, many publications address how far anomaly detection methods are able to meet the specific requirements of a cloud-based network. Since there is still no comprehensive overview of this constantly growing field of research, this literature review provides a systematic evaluation of 215 publications that can be considered as representative for the last ten years of this scientific development. Our analysis identifies three main methodological areas (machine learning, deep learning, statistical approaches) and summarizes how exactly the corresponding models are applied for the detection of anomalies. Furthermore, we clarify which concrete application areas are typically addressed by anomaly detection in the context of cloud computing environments and which related public datasets are often used for evaluations. Finally, we discuss the implications of the literature review and provide directions for future research.

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