Vortrag von: Vu Thuy Giang Do
Titel: Latent Distribution Based Anomaly Detection for Multivariate Time-Series Data
Zeit: 26.09.2025 - 10:00 - 11:00
Ort: WE5/05.005 (nicht hybrid)
Abstract:
Anomaly detection in telemetry data plays a crucial role, as any abnormal data can compromise the space monitoring system. To date, a variety of architectural models exist that can detect anomalies by reconstructing normal and abnormal data under autoencoder architectures and determine the presence of anomalies based on reconstruction error; the greater the reconstruction error, the more likely it is that an anomaly is present. These models focus on reducing reconstruction error while learning high-dimensional time-series data, such as telemetry data. However, recent models based on reconstruction error to detect abnormal data may be neither sufficient nor high-performance for multivariate time-series data, as outlier data points can also be reconstructed too well, resulting in an unexpectedly low reconstruction error. Moreover, how the data is represented affects the performance of a machine learning approach. This thesis applies the LatentOut unsupervised algorithm designed by Angiulli et al. (2022) to take advantage of the latent space data in combination with the reconstruction error representation to detect outliers in such complex data. The final result shows that the anomalies lie in a tendency in the sparsest region of the combined space.