亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

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

异常检测 计算机科学 系列(地层学) 异常(物理) 人工智能 背景(考古学) 时间序列 深度学习 卷积神经网络 机器学习 数据挖掘 模式识别(心理学) 凝聚态物理 生物 物理 古生物学
作者
Mohsin Munir,Shoaib Ahmed Siddiqui,Andreas Dengel,Sheraz Ahmed
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:7: 1991-2005 被引量:418
标识
DOI:10.1109/access.2018.2886457
摘要

Traditional distance and density-based anomaly detection techniques are unable to detect periodic and seasonality related point anomalies which occur commonly in streaming data, leaving a big gap in time series anomaly detection in the current era of the IoT. To address this problem, we present a novel deep learning-based anomaly detection approach (DeepAnT) for time series data, which is equally applicable to the non-streaming cases. DeepAnT is capable of detecting a wide range of anomalies, i.e., point anomalies, contextual anomalies, and discords in time series data. In contrast to the anomaly detection methods where anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to forecast the normal behavior of a time series. DeepAnT consists of two modules: time series predictor and anomaly detector. The time series predictor module uses deep convolutional neural network (CNN) to predict the next time stamp on the defined horizon. This module takes a window of time series (used as a context) and attempts to predict the next time stamp. The predicted value is then passed to the anomaly detector module, which is responsible for tagging the corresponding time stamp as normal or abnormal. DeepAnT can be trained even without removing the anomalies from the given data set. Generally, in deep learning-based approaches, a lot of data are required to train a model. Whereas in DeepAnT, a model can be trained on relatively small data set while achieving good generalization capabilities due to the effective parameter sharing of the CNN. As the anomaly detection in DeepAnT is unsupervised, it does not rely on anomaly labels at the time of model generation. Therefore, this approach can be directly applied to real-life scenarios where it is practically impossible to label a big stream of data coming from heterogeneous sensors comprising of both normal as well as anomalous points. We have performed a detailed evaluation of 15 algorithms on 10 anomaly detection benchmarks, which contain a total of 433 real and synthetic time series. Experiments show that DeepAnT outperforms the state-of-the-art anomaly detection methods in most of the cases, while performing on par with others.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
雪白的面包完成签到 ,获得积分10
6秒前
wanci应助Aaaaaa瘾采纳,获得10
15秒前
lixuebin完成签到 ,获得积分10
56秒前
闪闪妍发布了新的文献求助10
59秒前
绝尘完成签到,获得积分10
1分钟前
绝尘发布了新的文献求助20
1分钟前
科研通AI2S应助闪闪妍采纳,获得10
1分钟前
程住气完成签到 ,获得积分10
1分钟前
2分钟前
隐形曼青应助杰帅采纳,获得10
2分钟前
2分钟前
杰帅发布了新的文献求助10
2分钟前
田様应助杰帅采纳,获得10
3分钟前
3分钟前
shirley要奋斗完成签到 ,获得积分10
3分钟前
www完成签到,获得积分10
3分钟前
4分钟前
4分钟前
紫zi完成签到 ,获得积分10
4分钟前
lhjct0313完成签到 ,获得积分10
4分钟前
4分钟前
Aaaaaa瘾发布了新的文献求助10
4分钟前
丘比特应助Olivia采纳,获得10
4分钟前
5分钟前
5分钟前
Olivia发布了新的文献求助10
5分钟前
Hasee发布了新的文献求助10
5分钟前
5分钟前
杰帅发布了新的文献求助10
5分钟前
cc发布了新的文献求助10
5分钟前
bkagyin应助杰帅采纳,获得10
5分钟前
至乐无乐发布了新的文献求助10
5分钟前
赘婿应助abull采纳,获得10
6分钟前
6分钟前
OCDer发布了新的文献求助30
6分钟前
6分钟前
6分钟前
6分钟前
abull发布了新的文献求助10
6分钟前
小王好饿完成签到 ,获得积分10
7分钟前
高分求助中
The Oxford Handbook of Social Cognition (Second Edition, 2024) 1050
Kinetics of the Esterification Between 2-[(4-hydroxybutoxy)carbonyl] Benzoic Acid with 1,4-Butanediol: Tetrabutyl Orthotitanate as Catalyst 1000
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
Rechtsphilosophie 1000
Handbook of Qualitative Cross-Cultural Research Methods 600
Chen Hansheng: China’s Last Romantic Revolutionary 500
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3139573
求助须知:如何正确求助?哪些是违规求助? 2790439
关于积分的说明 7795297
捐赠科研通 2446910
什么是DOI,文献DOI怎么找? 1301487
科研通“疑难数据库(出版商)”最低求助积分说明 626248
版权声明 601146