  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. In this paper, the TSFEDL library is introduced. It compiles 20
state-of-the-art methods for both time series feature extraction and
prediction, employing convolutional and recurrent deep neural networks for its
use in several data mining tasks. The library is built upon a set of
Tensorflow+Keras and PyTorch modules under the AGPLv3 license. The performance
validation of the architectures included in this proposal confirms the
usefulness of this Python package.
