Search for: All records

Classifying multivariate time series (MTS), which record the values of multiple variables over a continuous period of time, has gained a lot of attention. However, existing techniques suffer from two major issues. First, the long-range dependencies of the time-series sequences are not well captured. Second, the interactions of multiple variables are generally not represented in features. To address these aforementioned issues, we propose a novel Cross Attention Stabilized Fully Convolutional Neural Network (CA-SFCN) to classify MTS data. First, we introduce a temporal attention mechanism to extract long- and short-term memories across all time steps. Second, variable attention is designed to select relevant variables at each time step. CA-SFCN is compared with 16 approaches using 14 different MTS datasets. The extensive experimental results show that the CA-SFCN outperforms state-of-the-art classification methods, and the cross attention mechanism achieves better performance than other attention mechanisms. 

Change point detection is widely used for finding transitions between states of data generation within a time series. Methods for change point detection currently assume this transition is instantaneous and therefore focus on finding a single point of data to classify as a change point. However, this assumption is flawed because many time series actually display short periods of transitions between different states of data generation. Previous work has shown Bayesian Online Change Point Detection (BOCPD) to be the most effective method for change point detection on a wide range of different time series. This paper explores adapting the change point detection algorithms to detect abrupt changes over short periods of time. We design a segment-based mechanism to examine a window of data points within a time series, rather than a single data point, to determine if the window captures abrupt change. We test our segment-based Bayesian change detection algorithm on 36 different time series and compare it to the original BOCPD algorithm. Our results show that, for some of these 36 time series, the segment-based approach for detecting abrupt changes can much more accurately identify change points based on standard metrics. 

Multivariate time series (MTS) are collected for different variables in studying scientific phenomena or monitoring system health where one time series records the values of one variable for a time period. Among the different variables, it is common that only a few variables contribute significantly to a specific phenomenon. Furthermore, the variables contributing significantly to different phenomena are often different. We denote the different variables that contribute to the occurrences of different phenomena as Phenomenon-specific Variables (PVs). In this paper, we formulate a novel problem of identifying significant PVs from MTS datasets. To analyze MTS data, feature extraction techniques have been extensively studied. However, most of them identify important global features for one dataset and do not utilize the temporal order of time series. To solve the newly introduced problem, we propose a solution framework, CNNmts-X, which is a new variant of the Convolutional Neural Networks (CNN) and can embed other feature extraction techniques (as X). Furthermore, we design a CNNmts-LR method that implements a new feature identification approach(LR) as X in the CNNmts-X framework. The LR method leverages both Linear Discriminant Analysis (LDA) and Random Forest (RF). Our extensive experiments on five real datasets show that the CNNmts-LR method has exhibited much better performance than several other baseline methods. Using 30% of the PVs discovered from the CNNmts-LR, classifications can achieve better or simila performance than using all the variables.