scholarly journals Anomaly detection in server metrics with use of one-sided median algorithm

2017 ◽  
Vol 9 (1) ◽  
pp. 5-22
Author(s):  
Szymon Zacher ◽  
Przemysław Ryba
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Detection Rate ◽  
Short Description ◽  
Algorithm Performance ◽  
Mail Service ◽  
Over Time ◽  
Simple Production ◽  
Good Detection

AbstractIn this paper we consider the problem of anomaly detection over time series metrics data took from one of corporate grade mail service cluster. We propose the algorithm based on one-sided median concept and present some results of experiments showing impact of parameters settings on algorithm performance. In addition we present short description of classes of anomalies discovered in monitored system. Proposed one-sided median based algorithm shows great robustness and good detection rate and can be considered as possible simple production ready solution.

scholarly journals Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning

2021 ◽  
Vol 11 (15) ◽  
pp. 6698
Author(s):  
Jehn-Ruey Jiang ◽  
Jian-Bin Kao ◽  
Yu-Lin Li
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Stationary Time Series ◽  
Test Time ◽  
Pearson Product Moment Correlation ◽  
Novel Technologies ◽  
Univariate Time Series ◽  
Comparison Results ◽  
Over Time

Thanks to the advance of novel technologies, such as sensors and Internet of Things (IoT) technologies, big amounts of data are continuously gathered over time, resulting in a variety of time series. A semi-supervised anomaly detection framework, called Tri-CAD, for univariate time series is proposed in this paper. Based on the Pearson product-moment correlation coefficient and Dickey–Fuller test, time series are first categorized into three classes: (i) periodic, (ii) stationary, and (iii) non-periodic and non-stationary time series. Afterwards, different mechanisms using statistics, wavelet transform, and deep learning autoencoder concepts are applied to different classes of time series for detecting anomalies. The performance of the proposed Tri-CAD framework is evaluated by experiments using three Numenta anomaly benchmark (NAB) datasets. The performance of Tri-CAD is compared with those of related methods, such as STL, SARIMA, LSTM, LSTM with STL, and ADSaS. The comparison results show that Tri-CAD outperforms the others in terms of the precision, recall, and F1-score.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

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

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith 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.

scholarly journals AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series

2021 ◽  
Author(s):  
Lin Zhang ◽  
Wenyu Zhang ◽  
Maxwell J. McNeil ◽  
Nachuan Chengwang ◽  
David S. Matteson ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Multivariate Time Series ◽  
Aurora A ◽  
Unified Framework
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