Anomaly detection using piecewise aggregate approximation in the amplitude domain

Ren, Hongliang; Liao, Xiujuan; Li, Zhiwu; AI-Ahmari, Abdulrahman

doi:10.1007/s10489-017-1017-x

Cited by 19 publications

(14 citation statements)

References 44 publications

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“…where is the number of samples, is the filter window size, and ≤ ≤ ( − ) . By removing abnormal data, null compensation and filtering, the original data need to be preprocessed for subsequent segmentation, shown in (21).…”

Section: B Data Preprocessingmentioning

confidence: 99%

“…In order to fully recognize the human posture, it is necessary to segment the complex postures into simple ones [17]- [19]. The time series segmentation algorithm can be divided into three methods: limiting the number of segments [20], [21], fitting error of a given segment [22], and segmenting by special points [23]. The special point segmentation uses the characteristics of the sequence itself to determine the segmentation point, which can avoid the omission of important information.…”

Section: Introductionmentioning

confidence: 99%

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Motion Recognition Based on Sum of the Squared Errors Distribution

Yang

Cui

et al. 2021

IEEE Access

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Human motion recognition is playing an increasingly important role in modern society. Direct recognition of complex motions has great limitations, so we usually study basic motions first. The key to the establishment of the basic motion set is how to determine the number of basic motions. Sum of the Squared Errors Distribution (SSED) is therefore proposed to determine the number of clustering classes of Self Organizing Maps (SOM). Secondly, the Weighted Tangent Segmentation (WTS) is also proposed to segment complex motions into simple ones, and the sequence with the time stamp is generated. Finally, Back Propagation with the time stamp (BPTS) is proposed to classify the simple motions according to the basic motion set, and the complex motion is recognized according to the time stamp. The motions of human upper limbs are used to verify the effectiveness of this method. SSED determines that the number of clusters of human upper limb motion is 10. The experimental results show that the correct segmentation rate (CSR) is 98.13%. The recognition rate of basic motions is 98.67% and 99.33% in user independent (UI) and user dependent (UD) experiments, respectively. The recognition rate of complex motions is 96.37%. Experiments on UI and UD recognitions verified the effectiveness of our algorithm. Compared with other recognition algorithms, the complex motion recognition method proposed in this paper has better recognition performance.

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Section: B Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Motion Recognition Based on Sum of the Squared Errors Distribution

Yang

Cui

et al. 2021

IEEE Access

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show abstract

“…The most widely employed approach for time-series representation is dimensionality reduction [ 1 , 2 , 3 , 4 , 5 , 6 ]. One of the initially used dimensionality reduction approaches is sampling [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

“…PAA has been demonstrated to be effective for time-series representations. Consequently, various extensions have been introduced in time-series representations [ 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification

Yang

Kang

2022

Sensors

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Time-series representation is the most important task in time-series analysis. One of the most widely employed time-series representation method is symbolic aggregate approximation (SAX), which converts the results from piecewise aggregate approximation to a symbol sequence. SAX is a simple and effective method; however, it only focuses on the mean value of each segment in the time-series. Here, we propose a novel time-series representation method—distance- and momentum-based symbolic aggregate approximation (DM-SAX)—that can secure time-series distributions by calculating the perpendicular distance from the time-axis to each data point and consider the time-series trend by adding a momentum factor reflecting the direction of previous data points. Experimental results for 29 highly imbalanced classification problems on the UCR datasets revealed that DM-SAX affords the optimal area under the curve (AUC) among competing time-series representation methods (SAX, extreme-SAX, overlap-SAX, and distance-based SAX). We statistically verified that performance improvements resulted in significant differences in the rankings. In addition, DM-SAX yielded the optimal AUC for real-world wire cutting and crimping process dataset. Meaningful data points such as outliers could be identified in a time-series outlier detection framework via the proposed method.

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“…It can be divided into three modes according to the availability of data labels: supervised anomaly detection, semi-supervised anomaly detection, and unsupervised anomaly detection [1]. Anomaly Detection using Piecewise Aggregate approximation in the Amplitude Domain (called ADPAAD) [2] is important unsupervised anomaly detection. It has been used in various fields, as a widely used method in anomaly detection of sequences, such as medical science [3], network security [4], finance [5], industrial engineering [6], and transportation [7].…”

Section: Introductionmentioning

confidence: 99%

Quantum Algorithm for Anomaly Detection of Sequences

Guo¹,

Liu²,

Pan³

et al. 2022

Preprint

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Anomaly detection of sequences is a hot topic in data mining. Anomaly Detection using Piecewise Aggregate approximation in the Amplitude Domain (called ADPAAD) is one of the widely used methods in anomaly detection of sequences. The core step in the classical algorithm for performing ADPAAD is to construct an approximate representation of the subsequence, where the elements of each subsequence are divided into several subsections according to the amplitude domain and then the average of the subsections is computed. It is computationally expensive when processing large-scale sequences. In this paper, we propose a quantum algorithm for ADPAAD, which can divide the subsequence elements and compute the average in parallel. Our quantum algorithm can achieve polynomial speedups on the number of subsequences and the length of subsequences over its classical counterpart.

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Anomaly detection using piecewise aggregate approximation in the amplitude domain

Cited by 19 publications

References 44 publications

Motion Recognition Based on Sum of the Squared Errors Distribution

Motion Recognition Based on Sum of the Squared Errors Distribution

Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification

Quantum Algorithm for Anomaly Detection of Sequences

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