Anomaly Detection for Bivariate Signals

Transactions on Large-Scale Data- and Knowledge-Centered Systems L, 2021

Data mining has become an important task for researchers in the past few years, including detecting anomalies that may represent events of interest. The problem of anomaly detection refers to discovering the patterns that do not conform to expected behavior. This paper analyzes recent studies on the detection of anomalies in time series. The goal is to provide an introduction to anomaly detection and a survey of recent research and challenges. The article is divided into three main parts. First, the main concepts are presented. Then, the anomaly detection task is defined. Afterward, the main approaches and strategies to solve the problem are presented.

IEEE Access

Anomaly detection is essential for the monitoring and improvement of product quality in manufacturing processes. In the case of semiconductor manufacturing, where large amounts of time series data from equipment sensors are rapidly accumulated, identifying anomalous signals within this data presents a significant challenge. The data in question is multivariate and of varying lengths, with an often highly imbalanced ratio of normal to abnormal signals. Given the nature of this data, traditional data-driven methods may not be appropriate for its analysis. This paper proposes a novel unsupervised anomaly detection model for the analysis of multivariate time series data. The model utilizes a unique recurrent neural network architecture and a special objective function to detect anomalies. Furthermore, a relevance analysis method is introduced to facilitate the interpretation and analysis of the detected anomalous signals. Our experimental results indicate that this deep anomaly detection model, which summarizes sensor data of different lengths into a low-dimensional latent space, enabling the easy visualization and distinction of anomalous signals, can be applied in real-world semiconductor manufacturing factories and used by on-site engineers for both analysis and execution purposes. INDEX TERMS Anomaly detection, fault detection and diagnosis, multivariate time series data, semiconductor manufacturing, unsupervised learning.

International Journal of Computer Science and Mobile Computing

The detection of Anomaly in time series data has gained a significant amount of popularity due to its indulgence in various domains of industry such as healthcare, the financial sector, industrial services, and other several reasons. This paper represents an elaborate survey of varied anomaly detection techniques that are tested on time series data. The aim of the paper is to identify various anomalous outlines in the dataset through a number of different approaches, algorithms, and techniques. At the beginning of the paper, the importance of detecting anomalies in real-world scenarios is explained followed by describing the efficient techniques capable of dealing with it. The paper covers all the aspects of anomaly detection starting from traditional analytical and statistical models to modern-day advanced machine learning methods. The moving averages, exponential smoothening, and process control which are considered to be the traditional approaches for detecting anomalies in Machi...

Proceedings of the 17th international conference on Scientific and statistical database management, 2005

Recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time. However, because of the sheer volume of data most of it will never be inspected by an algorithm, much less a human being. One way to mitigate this problem is to perform ...

Journal of Networking and Network Applications

Anomaly detection in multivariate time series is an important research direction, which helps to improve the security of industrial systems by detecting abnormally unreliable devices. Multivariate time series (MTS) anomalies not only need to pay attention to the time correlation between different time series but also need to consider the abnormal changes in the relationship between different variables. Once the influence relationship between two variables that influence each other is ignored, it will likely lead to false positives or false positives. At the same time, the degree of influence between different time series or different features is also inconsistent, just like what happened recently have radically different influences on the present. Furthermore, most of the existing models are weak in detecting no abnormality. To tackle these issues, in this paper, we propose a new model of multivariate time series anomaly detection based on reconstruction and forecast, named MTAD RF....

2005

Our goal is to generate comprehensible and accurate models from multiple time series for anomaly detection. The models need to produce anomaly scores in an online manner for real-life monitoring tasks. We introduce three algorithms that work in a constructed feature space and evaluate them with a real data set from the NASA shuttle program. Our offline and online evaluations indicate that our algorithms can be more accurate than two existing algorithms.

Cornell University - arXiv, 2022

Unsupervised anomaly detection in time-series has been extensively investigated in the literature. Notwithstanding the relevance of this topic in numerous application fields, a complete and extensive evaluation of recent state-of-the-art techniques is still missing. Few efforts have been made to compare existing unsupervised time-series anomaly detection methods rigorously. However, only standard performance metrics, namely precision, recall, and F1-score are usually considered. Essential aspects for assessing their practical relevance are therefore neglected. This paper proposes an original and in-depth evaluation study of recent unsupervised anomaly detection techniques in time-series. Instead of relying solely on standard performance metrics, additional yet informative metrics and protocols are taken into account. In particular, (1) more elaborate performance metrics specifically tailored for time-series are used; (2) the model size and the model stability are studied; (3) an analysis of the tested approaches with respect to the anomaly type is provided; and (4) a clear and unique protocol is followed for all experiments. Overall, this extensive analysis aims to assess the maturity of state-of-the-art time-series anomaly detection, give insights regarding their applicability under real-world setups and provide to the community a more complete evaluation protocol.

arXiv: Applications, 2020

One of the contemporary challenges in anomaly detection is the ability to detect, and differentiate between, both point and collective anomalies within a data sequence or time series. The anomaly package has been developed to provide users with a choice of anomaly detection methods and, in particular, provides an implementation of the recently proposed CAPA family of anomaly detection algorithms. This article describes the methods implemented whilst also highlighting their application to simulated data as well as real data examples contained in the package.

Journal of Artificial Intelligence Research, 2021

The existence of an anomaly detection method that is optimal for all domains is a myth. Thus, there exists a plethora of anomaly detection methods which increases every year for a wide variety of domains. But a strength can also be a weakness; given this massive library of methods, how can one select the best method for their application? Current literature is focused on creating new anomaly detection methods or large frameworks for experimenting with multiple methods at the same time. However, and especially as the literature continues to expand, an extensive evaluation of every anomaly detection method is simply not feasible. To reduce this evaluation burden, we present guidelines to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays such as seasonality, trend, level change concept drift, and missing time steps. We provide a comprehensive experimental validation and survey of twelve anomaly detection methods over different time series characteristics to form guidelines based on several metrics: the AUC (Area Under the Curve), windowed F-score, and Numenta Anomaly Benchmark (NAB) scoring model. Applying our methodologies can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods, especially in an online setting. 1. Similar analysis (Emmott et al., 2015) has been performed before to compute the influence of meta-data on anomaly detection but for feature-vector-based datasets instead of time series. 2. See Appendix for a table of all acronyms and their definitions. 3. See https://github.com/dn3kmc/jair anomaly detection for all source code implementations, Jupyter notebooks demonstrating how to determine characteristics, and datasets.

2020

Industrial product testing is frequently performed in cycles, resulting in cycle-dependent test data. Monitoring the condition of products under test involves analysis of large and complex test data sets. Main tasks are to detect anomalies and dependencies between observation variables, which appears to be challenging to engineers. In this paper, we present a flexible and extendable visual analytics approach for anomaly detection focusing on cycle-depended data. It is based on a glyph representation to visualize anomaly scores of cycles with respect to interactively selected reference data. Our approach is built on a design study in collaboration with an industrial engineering corporation, and is demonstrated on real data from engines tested on automotive testbeds. Based on findings from evaluation results, we provide a discussion and an outlook for future work.