A5087188919- Time Series Analysis and Forecasting
- Anomaly Detection Techniques and Applications
- Data Stream Mining Techniques
- Network Security and Intrusion Detection
- Complex Systems and Time Series Analysis
- Traffic Prediction and Management Techniques
Nanjing University
2021-2022
Anomaly detection on multivariate time series (MTS) is an important research topic in data mining, which has a wide range of applications information technology, financial management, manufacturing system, and so on. However, the state-of-the-art unsupervised deep learning models for MTS anomaly are vulnerable to noise have poor performance training containing anomalies. In this article, we propose novel Self-Training based Detection with Generative Adversarial Network (GAN) model called...
Multivariate time series (MTS) clustering is an important technique for discovering co-evolving patterns and interpreting group characteristics in many areas including economics, bioinformatics, data science, etc. Although has been widely studied the past decades, no enough attention paid to capture time-varying correlation MTS. In this article, we propose a novel approach MTS based on features. We introduce Gaussian Markov Random Fields (T-GMRF) model describe structure between variables,...