LSTM-based Anomaly Detection for Non-linear Dynamical System

Anomaly detection for non-linear dynamical system plays an important role in ensuring the system stability. However, it is usually complex and has to be solved by large-scale simulation which requires extensive computing resources. In this paper, we propose a novel anomaly detection scheme in non-linear dynamical system based on Long Short-Term Memory (LSTM) to capture complex temporal changes of the time sequence and make multi-step predictions. Specifically, we first present the framework of LSTM-based anomaly detection in non-linear dynamical system, including data preprocessing, multi-step prediction and anomaly detection. According to the prediction requirement, two types of training modes are explored in multi-step prediction, where samples in a wall shear stress dataset are collected by an adaptive sliding window. On the basis of the multi-step prediction result, a Local Average with Adaptive Parameters (LAAP) algorithm is proposed to extract local numerical features of the time sequence and estimate the upcoming anomaly. The experimental results show that our proposed multi-step prediction method can achieve a higher prediction accuracy than traditional method in wall shear stress dataset, and the LAAP algorithm performs better than the absolute value-based method in anomaly detection task.Comment: 8 pages, 6 figure

Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?

Anomaly detection in time series is a complex task that has been widely studied. In recent years, the ability of unsupervised anomaly detection algorithms has received much attention. This trend has led researchers to compare only learning-based methods in their articles, abandoning some more conventional approaches. As a result, the community in this field has been encouraged to propose increasingly complex learning-based models mainly based on deep neural networks. To our knowledge, there are no comparative studies between conventional, machine learning-based and, deep neural network methods for the detection of anomalies in multivariate time series. In this work, we study the anomaly detection performance of sixteen conventional, machine learning-based and, deep neural network approaches on five real-world open datasets. By analyzing and comparing the performance of each of the sixteen methods, we show that no family of methods outperforms the others. Therefore, we encourage the community to reincorporate the three categories of methods in the anomaly detection in multivariate time series benchmarks

Advanced Data Analytics Methodologies for Anomaly Detection in Multivariate Time Series Vehicle Operating Data

Early detection of faults in the vehicle operating systems is a research domain of high significance to sustain full control of the systems since anomalous behaviors usually result in performance loss for a long time before detecting them as critical failures. In other words, operating systems exhibit degradation when failure begins to occur. Indeed, multiple presences of the failures in the system performance are not only anomalous behavior signals but also show that taking maintenance actions to keep the system performance is vital. Maintaining the systems in the nominal performance for the lifetime with the lowest maintenance cost is extremely challenging and it is important to be aware of imminent failure before it arises and implement the best countermeasures to avoid extra losses. In this context, the timely anomaly detection of the performance of the operating system is worthy of investigation. Early detection of imminent anomalous behaviors of the operating system is difficult without appropriate modeling, prediction, and analysis of the time series records of the system. Data based technologies have prepared a great foundation to develop advanced methods for modeling and prediction of time series data streams. In this research, we propose novel methodologies to predict the patterns of multivariate time series operational data of the vehicle and recognize the second-wise unhealthy states. These approaches help with the early detection of abnormalities in the behavior of the vehicle based on multiple data channels whose second-wise records for different functional working groups in the operating systems of the vehicle. Furthermore, a real case study data set is used to validate the accuracy of the proposed prediction and anomaly detection methodologies