PP: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

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PP: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

PROBLEM: OmniAnomaly multivariate time series anomaly detection + unsupervised 主体思想: input: multivariate time series to RNN ------> capture the normal patterns -----> reconstruct input data by the representations ------> use the reconstruction pr…

PP: Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications

Problem: unsupervised anomaly detection for seasonal KPIs in web applications. Donut: an unsupervised anomaly detection algorithm based on VAE. Background: 有的time series data have seasonal patterns occurring at regular intervals. Data: KPI shapes: se…

PP: A dual-stage attention-based recurrent neural network for time series prediction

Problem: time series prediction The nonlinear autoregressive exogenous model: The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values…

"Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network" 解读

简介:这是一篇17年的CVPR,作者提出使用现有的人脸识别深度神经网络Resnet101来得到一个具有鲁棒性的人脸模型. 原文链接:https://www.researchgate.net/publication/311668561_Regressing_Robust_and_Discriminative_3D_Morphable_Models_with_a_very_Deep_Neural_Network 摘要主要说了两个部分:第一部分,三维人脸模型还没有广泛应用到人脸识别等领域,主要原因是…

Anomaly Detection for Time Series Data with Deep Learning——本质分类正常和异常的行为，对于检测异常行为，采用预测正常行为方式来做

A sample network anomaly detection project Suppose we wanted to detect network anomalies with the understanding that an anomaly might point to hardware failure, application failure, or an intrusion. What our model will show us The RNN will train on a…

Machine Learning No.10: Anomaly detection

1. Algorithm 2. evaluating an anomaly detection system 3. anomaly detection vs supervised learning 4. choose what features to use. - choose the features xi which hist(xi) is like gaussian shape, or transfer xi such as log(xi+c) to make hist(xi) to be…

PP: Time series anomaly detection with variational autoencoders

Problem: unsupervised anomaly detection Model: VAE-reEncoder VAE with two encoders and one decoder. They use bidirectional bow-tie LSTM for each part. Why use bow-tie model: to remove noise to some extent when encoding.…

Time Series Anomaly Detection

这里有个2015年的综述文章,概括的比较好,各种技术的适用场景. https://iwringer.wordpress.com/2015/11/17/anomaly-detection-concepts-and-techniques/ 其中 Clustering 技术可以使用 K-Means, Gaussian Mixture Model. GMM 模型可以参考这个很棒的文章 https://colab.research.google.com/github/jakevdp/PythonData…

PP: Deep r -th Root of Rank Supervised Joint Binary Embedding for Multivariate Time Series Retrieval

from: Dacheng Tao 悉尼大学 PROBLEM: time series retrieval: given the current multivariate time series segment, how to obtain its relevant time series segments in the historical data. Two challenging: 1. it requires a compact representation of the raw tim…

PP: Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

From: Stanford University; Jure Leskovec, citation 6w+; Problem: subsequence clustering. Challenging: discover patterns is challenging because it requires simultaneous segmentation and clustering of the time series + interpreting the cluster results…