time-series-6

Time Series Anomaly Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in time-series-anomaly-detection-12

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Dataset

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UCR Anomaly Archive

Libraries

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Use these libraries to find time-series-anomaly-detection-12 models and implementations

sintel-dev/orion

3 papers 987

Datasets

UCR Anomaly Archive

ATMs fault prediction

ODDS

FedTADBench

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Most implemented papers

Glow: Generative Flow with Invertible 1x1 Convolutions

Diederik P. Kingma, Prafulla Dhariwal•Sun Jul 08 2018

Glow, a simple type of generative flow using an invertible 1x1 convolution, is proposed, demonstrating that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images.

3444

Content

signals-dev/Orion

2 papers 987

xuhongzuo/DeepOD

2 papers 321

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Paper Graph

XGBoost: A Scalable Tree Boosting System

Tianqi Chen, Carlos Guestrin•Tue Mar 08 2016

This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.

48813 0

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LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

G. Anand, L. Vig, Pankaj Malhotra, Anusha Ramakrishnan, Puneet Agarwal, Gautam M. Shroff•Thu Jun 30 2016

This work proposes a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct 'normal' time-series behavior, and thereafter uses reconstruction error to detect anomalies.

1105 0

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Deep and Confident Prediction for Time Series at Uber

Lingxue Zhu, N. Laptev•Tue Sep 05 2017

A novel end-to-end Bayesian deep model is proposed that provides time series prediction along with uncertainty estimation at Uber and is successfully applied to large-scale time series anomaly detection at Uber.

366 0

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A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

N. Chawla, Haifeng Chen, Bo Zong, Wei Cheng, Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, C. Lumezanu, Jingchao Ni•Mon Nov 19 2018

This paper proposes a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data and demonstrates that MSCRED can outperform state-of-the-art baseline methods.

846 0

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TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

K. Veeramachaneni, Alfredo Cuesta-Infante, Sarah Alnegheimish, Dongyu Liu, Alexander Geiger•Tue Sep 15 2020

TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) that can effectively detect anomalies and outperform baseline methods in most cases, and has the highest averaged F1 score across all the datasets.

261 0

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

Shoaib Ahmed Siddiqui, A. Dengel, Sheraz Ahmed, Mohsin Munir•Mon Dec 31 2018

A novel deep learning-based anomaly detection approach (DeepAnT) for time series data, which is equally applicable to the non-streaming cases and outperforms the state-of-the-art anomaly detection methods in most of the cases, while performing on par with others.

557 0

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Time-Series Anomaly Detection Service at Microsoft

Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang•Sun Jun 09 2019

This work is the first attempt to borrow the SR model from visual saliency detection domain to time-series anomaly detection, and innovatively combine SR and CNN together to improve the performance of SR model.

600 0

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Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Jianmin Wang, Haixu Wu, Mingsheng Long, Jiehui Xu•Tue Oct 05 2021

The Anomaly Transformer achieves state-of-the-art results on six unsupervised time series anomaly detection benchmarks of three applications: service monitoring, space&earth exploration, and water treatment.

835 0

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