network (CNN), recurrent neural network (RNN), and Transformer), two generative (variational autoencoder (VAE) and generative …

machine learning approach of an autoencoder. In addition, we focus on the model parameters of the intermediate layer of the … neural network that constitute the autoencoder and confirm that the three automatically generated factors represent the … bonds by setting their valuation with the autoencoder, and we confirm good performance compared with the trend …

) serving interbank funds transfers. We develop an autoencoder for the Sistema de Pagos Interbancarios (SPI) of Ecuador, which …. (2018) and Sabetti and Heijmans (2020). We train four different autoencoder models using intraday data structured in three … simulations to feature a baseline scenario and identify relevant autoencoder parametrizations for anomaly detection. The main …

machine learning approach of an autoencoder. In addition, we focus on the model parameters of the intermediate layer of the … neural network that constitute the autoencoder and confirm that the three automatically generated factors represent the … bonds by setting their valuation with the autoencoder, and we confirm good performance compared with the trend …

novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be …