MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series

Anomaly detection of time series, especially multivariate time series(time series with multiple sensors), has been focused on for several years. Though existing method has achieved great progress, there are several challenging problems to be solved. Firstly, existing method including neural network only concentrate on the relationship in terms of timestamp. To be exact, they only want to know how does the data in the past influence which in the future. However, one sensor sometimes intervenes in other sensor such as the speed of wind may cause decrease of temperature. Secondly, there exist two categories of model for time series anomaly detection: prediction model and reconstruction model. Prediction model is adept at learning timely representation while short of capability when faced with sparse anomaly. Conversely, reconstruction model is opposite. Therefore, how can we efficiently get the relationship both in terms of both timestamp and sensors becomes our main topic. Our approach uses GAT, which is originated from graph neural network, to obtain connection between sensors. And LSTM is used to obtain relationships timely. Our approach is also designed to be double headed to calculate both prediction loss and reconstruction loss via VAE(Variational Auto-Encoder). In order to take advantage of two sorts of model, multi-task optimization algorithm is used in this model

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework

이상치 탐지를 위한 적대적 사전 학습 알고리즘

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 박종우.In this thesis, we propose a semi-supervised dictionary learning algorithm that learns representations of only non-outlier data. The presence of outliers in a dataset is a major drawback for dictionary learning, resulting in less than desirable performance in real-world applications. Our adversarial dictionary learning (ADL) algorithm exploits a supervision dataset composed of known outliers. The algorithm penalizes the dictionary expressing the known outliers well. Penalizing the known outliers makes dictionary learning robust to the outliers present in the dataset. The proposed method can handle highly corrupted dataset which cannot be effectively dealt with using conventional robust dictionary learning algorithms. We empirically show the usefulness of our algorithm with extensive experiments on anomaly detection, using both synthetic univariate time-series data and multivariate point data.본 논문에서는 이상치가 아닌 데이터의 희소 표현만을 학습하는 준지도 사전 학습 알고리즘을 제안한다. 데이터셋에 섞여 있는 이상치는 사전 학습의 주요한 문제로, 실제 문제에 적용 시 바람직하지 않은 성능을 초래한다. 본 연구에서 제안하는 적대적 사전 학습(ADL) 알고리즘은 이상치 데이터로 구성된 감독 데이터셋을 학습에 이용한다. 우리의 알고리즘은 주어진 이상치 데이터를 잘 표현하는 사전에 페널티를 주고, 이것은 사전이 학습 데이터셋에 섞여 있는 이상치에 강건하게 학습되도록 한다. 제안된 방법은 기존의 사전 학습 방법들과 비교해 이상치의 비중이 높은 데이터셋에서도 효과적으로 사전을 학습해 낸다. 이 연구에서는 인공적인 단변량 시계열 데이터와 다변량 점 데이터에 대한 이상치 탐지 실험을 통해 알고리즘의 유용성을 경험적으로 검증한다.1 Introduction 1 1.1 Related Works 4 1.2 Contributions of This Thesis 5 1.3 Organization 6 2 Sparse Representation and Dictionary Learning 7 2.1 Sparse Representation 7 2.1.1 Problem De nition of Sparse Representation 7 2.1.2 Sparse representation with l0-norm regularization 10 2.1.3 Sparse representation with l1-norm regularization 11 2.1.4 Sparse representation with lp-norm regularization (0 < p < 1) 12 2.2 Dictionary Learning 12 2.2.1 Problem De nition of Dictionary Learning 12 2.2.2 Dictionary Learning Methods 14 3 Adversarial Dictionary Learning 18 3.1 Problem Formulation 18 3.2 Adversarial Loss 19 3.3 Optimization Algorithm 20 4 Experiments 25 4.1 Data Description 26 4.1.1 Univariate Time-series Data 26 4.1.2 Multivariate Point Data 29 4.2 Evaluation Process 30 4.2.1 A Baseline of Anomaly Detection 30 4.2.2 ROC Curve and AUC 34 4.3 Experiment Setting 35 4.4 Results 36 5 Conclusion 43 Bibliography 45 국문초록 50Maste

Decomposable Principal Component Analysis

We consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute its computation. For this purpose, we reformulate the problem in the sparse inverse covariance (concentration) domain and solve the global eigenvalue problem using a sequence of local eigenvalue problems in each of the cliques of the decomposable graph. We demonstrate the application of our methodology in the context of decentralized anomaly detection in the Abilene backbone network. Based on the topology of the network, we propose an approximate statistical graphical model and distribute the computation of PCA

Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art on several metrics.Comment: Presented at 2nd ML for PHM Workshop at SIGKDD 2017, Halifax, Canad