2019/08/14 by Yifan Hao, Huiping Cao, Abdullah Mueen +1
Computer Science · Engineering · #Advanced Chemical Sensor Technologies #Anomaly Detection Techniques and Applications #Time Series Analysis and Forecasting
paper · pdf · doi:10.1109/tkde.2019.2934464
openalex publication_date 2019/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Multivariate time series (MTS) are collected for different variables in studying scientific phenomena or monitoring system health where each 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 Xin 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 percent of the PVs discovered from the CNNmts-LR, classifications can achieve better or similar performance than using all the variables.