Implementing and Optimizing a Variational Autoencoder (VAE) for Anomaly Detection in Multivariate Time-Series
- Problem Overview
This project implements a β-Variational Autoencoder (β-VAE) for anomaly detection in high-dimensional multivariate time-series data. The model is trained in an unsupervised manner to learn normal patterns in the data.
Anomalies are detected using a combined:
- Reconstruction Error