This program attempts to use an LSTM autoencoder to detect anomalies the daily closing prices of the S&P 500 index. The autoencoder is trained exclusively on normal data without anomalies. When new ...

What is an LSTM autoencoder? 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 ...

But using autoencoder, which have many variables with strong correlations, is said to cause a decline of detection power. To avoid the above problem, the technique to apply L1 regularization to LSTM ...

In this study, we apply deep learning to extract SESs and develop a novel deep learning network based on geoelectric field characteristics by combining the long short-term memory (LSTM) blocks with an ...

In order to solve the problem, this paper presents a two-stage hierarchical clustering algorithm based on a Long Short Term Memory (LSTM) autoencoder. Firstly, we use LSTM autoencoder to learn the ...

To tackle this problem, we present a long short-term memory-based adversarial variational autoencoder ... common structure of the data shared among subjects, making it suitable for cross-subject ...