TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)

2 code implementations • 7 Jun 2022 • Ignacio Aguilera-Martos, Ángel M. García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero, José Javier Valle-Alonso, Francisco Herrera

The combination of convolutional and recurrent neural networks is a promising framework that allows the extraction of high-quality spatio-temporal features together with its temporal dependencies, which is key for time series prediction problems such as forecasting, classification or anomaly detection, amongst others.

Anomaly Detection Time Series +1