Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep Learning

2020 ◽  
pp. 167-182
Author(s):  
Yildiz Karadayi
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Unsupervised Anomaly Detection ◽  
Spatio Temporal

scholarly journals A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data

2020 ◽  
Vol 10 (15) ◽  
pp. 5191
Author(s):  
Yıldız Karadayı ◽  
Mehmet N. Aydin ◽  
A. Selçuk Öğrenci
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Hurricane Katrina ◽  
Time Series Data ◽  
Hybrid Approach ◽  
Ground Truth ◽  
Outbreak Detection ◽  
Series Data ◽  
Detection Techniques ◽  
Spatio Temporal

Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection.

scholarly journals FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2451 ◽  
Author(s):  
Mohsin Munir ◽  
Shoaib Ahmed Siddiqui ◽  
Muhammad Ali Chattha ◽  
Andreas Dengel ◽  
Sheraz Ahmed
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Internal State ◽  
Arima Model ◽  
Streaming Data ◽  
Detection Technique ◽  
Detection Methods ◽  
Smart Devices ◽  
Detection Techniques ◽  
Unsupervised Anomaly Detection

The need for robust unsupervised anomaly detection in streaming data is increasing rapidly in the current era of smart devices, where enormous data are gathered from numerous sensors. These sensors record the internal state of a machine, the external environment, and the interaction of machines with other machines and humans. It is of prime importance to leverage this information in order to minimize downtime of machines, or even avoid downtime completely by constant monitoring. Since each device generates a different type of streaming data, it is normally the case that a specific kind of anomaly detection technique performs better than the others depending on the data type. For some types of data and use-cases, statistical anomaly detection techniques work better, whereas for others, deep learning-based techniques are preferred. In this paper, we present a novel anomaly detection technique, FuseAD, which takes advantage of both statistical and deep-learning-based approaches by fusing them together in a residual fashion. The obtained results show an increase in area under the curve (AUC) as compared to state-of-the-art anomaly detection methods when FuseAD is tested on a publicly available dataset (Yahoo Webscope benchmark). The obtained results advocate that this fusion-based technique can obtain the best of both worlds by combining their strengths and complementing their weaknesses. We also perform an ablation study to quantify the contribution of the individual components in FuseAD, i.e., the statistical ARIMA model as well as the deep-learning-based convolutional neural network (CNN) model.

scholarly journals A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

2022 ◽  
Vol 70 (3) ◽  
pp. 5363-5381
Author(s):  
Amgad Muneer ◽  
Shakirah Mohd Taib ◽  
Suliman Mohamed Fati ◽  
Abdullateef O. Balogun ◽  
Izzatdin Abdul Aziz
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Unsupervised Anomaly Detection

scholarly journals DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 1991-2005 ◽  
Author(s):  
Mohsin Munir ◽  
Shoaib Ahmed Siddiqui ◽  
Andreas Dengel ◽  
Sheraz Ahmed
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Learning Approach ◽  
Unsupervised Anomaly Detection

scholarly journals RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection

2021 ◽  
Author(s):  
Boyang Liu ◽  
Ding Wang ◽  
Kaixiang Lin ◽  
Pang-Ning Tan ◽  
Jiayu Zhou
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Missing Values ◽  
Deep Neural Networks ◽  
Reconstruction Error ◽  
Feature Representation ◽  
High Complexity ◽  
Unsupervised Anomaly Detection ◽  
Robust Framework

Unsupervised anomaly detection plays a crucial role in many critical applications. Driven by the success of deep learning, recent years have witnessed growing interests in applying deep neural networks (DNNs) to anomaly detection problems. A common approach is using autoencoders to learn a feature representation for the normal observations in the data. The reconstruction error of the autoencoder is then used as outlier scores to detect the anomalies. However, due to the high complexity brought upon by the over-parameterization of DNNs, the reconstruction error of the anomalies could also be small, which hampers the effectiveness of these methods. To alleviate this problem, we propose a robust framework using collaborative autoencoders to jointly identify normal observations from the data while learning its feature representation. We investigate the theoretical properties of the framework and empirically show its outstanding performance as compared to other DNN-based methods. Our experimental results also show the resiliency of the framework to missing values compared to other baseline methods.

P203 Unsupervised one-class classification and anomaly detection of stress echocardiograms with deep denoising spatio-temporal autoencoders

2020 ◽  
Vol 41 (Supplement_1) ◽  
Author(s):  
B C S Loh ◽  
A Y Y Fong ◽  
T K Ong ◽  
P H H Then
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Prior Knowledge ◽  
Wall Motion ◽  
Training Samples ◽  
Wall Motion Abnormalities ◽  
Spatio Temporal ◽  
Detection Of Stress ◽  
One Class Classification ◽  
Assessment Of Stress

Abstract Introduction The combination of medical knowledge, experience and AI algorithms have supported the advancement of patient care and the lowering of healthcare costs. Machine and deep learning methods enable the extraction of meaningful patterns that remain beyond human perception. Numerous computer-aided diagnosis and detection systems have been developed to assist in the assessment of stress echocardiograms. However, issues are encountered when facing imbalanced, limited, and unannotated datasets. Learning from imbalanced medical datasets impairs diagnostic accuracy due to classifier bias and overfitting. Furthermore, datasets comprising of all existing abnormal classes are impossible to obtain, hence supervised algorithms would fail to generate predictions for classes devoid of training samples. Moreover, reliance on prior knowledge in the form of expert annotation and anatomical region extraction impairs scalability, as these procedures are time-consuming, computationally expensive, and limited to specific tasks. Purpose We aimed to perform one-class classification and anomaly detection of stress echocardiograms using unsupervised deep learning techniques to discriminate between normal and abnormal videos as well as to localise wall motion abnormalities within individual frames. Methods Deep denoising spatio-temporal autoencoder networks were employed to learn visual and motion representations from multiple echocardiographic cross-sections and stress stages. Extracted middle layer features were modelled by one-class support vector machines to discriminate between regular and irregular echocardiograms despite the absence of abnormal training samples. Reconstruction errors allowed for direct visualisation and localisation of anomalous cardiac regions, without the need for annotated training data or segmentation of structures. Results 2D B-mode stress echocardiograms acquired from 36 patients were classified as normal or abnormal based on patient reports and served as the ground truth. Results revealed that learnt features extracted from spatio-temporal autoencoders trained solely on normal data can be utilised to classify abnormal echocardiograms with a high level of accuracy, sensitivity and specificity. In addition to that, as validated by an expert reader, spatio-temporal autoencoder reconstruction errors were capable of detecting and localising wall motion abnormalities in specific cardiac regions without prior knowledge of abnormal segments. Conclusions The trained model enables the classification and detection of spatio-temporal abnormalities in stress echocardiograms. Therefore, the proposed networks have the potential of assisting in the global and regional assessment of stress echocardiograms.

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