This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...

However, the field of industrial multivariate time series anomaly detection still faces two main challenges: (1) the lack of more efficient representation methods for multivariate time series data; (2 ...

The initial implementation uses a convolutional autoencoder (CAE) model, as shown in Fig. 1, trained on electrical and electromagnetic time series data for anomaly detection in wind turbine ...

The structure of modern industrial equipment is usually complex, which will lead to data explosion and multivariate time series problems. An approach of anomaly detection for multivariate time series ...

LSTM autoencoder is an encoder that makes use of LSTM encoder-decoder architecture to compress data using an encoder and decode it to retain original structure using a decoder. by Ankit Das Simple ...

Adaptive Temporal–Spatial Pyramid Variational Autoencoder Model for Multirate Dynamic Chemical Process Soft Sensing Application. Bingbing Shen. ... This structure not only selectively utilizes ...