… performance for the anomaly detection on multivariate time series. The most typical method
for … open issues for anomaly detection in multivariate time series that detect anomalies at an …

… We propose STADN to deal with the challenges faced by anomaly detection in multivariate
time series. As illustrated in Fig. 1, STADN contains two essential components: Prediction …

… shape anomaly detection for multivariate time series. The extended version of Fuzzy C-Means
is used to capture the structure of multivariate time … The reconstruction of the multivariate …

… for anomaly detection in this paper, the focus is on reducing the amount of information required
… adaptiveness: Typically, anomaly detection models require periodic retraining to adapt to …

… In this article we present a comparative evaluation of a large variety of anomaly detection
algorithms. Clearly, anomaly detection performance is one very important factor for algorithm …

… Anomaly detection has been an active research topic in … In this paper, we focus on anomaly
detection for multivariate … ’s behavioral anomalies can be timely detected and later resolved. …

… In this paper, we propose a new method called UnSupervised Anomaly Detection for
multivariate time series (USAD) based on an autoencoder architecture [15] whose learning is …

… We compute the performance of the three anomaly detection methods AE, LSTM-VAE and
USAD on a transformed univariate time series obtained using only non-local information, ie …

… network data that features spatial and temporal information, network … an information-theoretic
model for anomaly detection across varying dimensions, displaying highlighted anomalies …

… multivariate time series (MTS) data is being produced daily by industrial systems, corresponding
in many cases to life-critical tasks. The recent anomaly detection … for anomaly detection …