Jun 11, 2020 · Here we will learn the details of data preparation for LSTM models, and build an LSTM Autoencoder for rare-event classification.

Jun 2, 2020 · In this post, we will learn how we can use a simple dense layers autoencoder to build a rare event classifier. The purpose of this post is to demonstrate the implementation of an Autoencoder for extreme rare-event classification.

Feb 20, 2021 · Autoencoder mainly consist of three main parts: 1) Encoder, which tries to reduce data dimensionality. 2) Code, which is the compressed representation of the data. 3) Decoder, which tries to revert the data into the original form without losing much information.

Oct 23, 2019 · The proposed approach combines an autoencoder to detect rare events and a long short-term memory (LSTM) network to identify the types of faults. The simple autoencoder trains the model with only normal data and evaluates input variables to …

In this paper, we propose a hybrid autoencoder model, called ConvBiLSTM-AE, which combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) to more effectively train complex temporal data patterns in anomaly detection.

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 permanent-magnet generators.

Feb 14, 2025 · The framework leverages the machine learning autoencoder model for anomaly detection and DBSCAN from the clustering domain for high-level event mining. Our approach is flexible in terms of parameters and input and can be adjusted to a user’s requirements.

Apr 25, 2025 · Anomaly Detection (AD) defines the task of identifying observations or events that deviate from typical - or normal - patterns, a critical capability in IT security for recognizing incidents such as system misconfigurations, malware infections, or cyberattacks. In enterprise environments like SAP HANA Cloud systems, this task often involves monitoring high-dimensional, multivariate time series ...

Apr 12, 2025 · LSTM autoencoders are a powerful tool for detecting anomalies in sequential data. They leverage the strengths of Long Short-Term Memory (LSTM) networks to capture temporal dependencies, making them particularly effective for time series data.

1 day ago · A previous ATLAS search applied an event-level unsupervised autoencoder-based anomaly detection algorithm to search for resonances for several di-object combinations, ... After training, we oversample events from the background model to generate synthetic background events in the SR at 3 times the amount of data in the SR. This oversampling ...