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.

本研究介绍了 TSFEDL 库，该库编译了 20 种时序特征提取和预测的先进方法，利用卷积神经网络和循环神经网络用于数据挖掘任务，结果表明该 Python 库有很高的实用价值。

TSFEDL: 一个 Python 库，用于使用深度学习进行时间序列时空特征提取和预测（并带有有关详细网络架构和研究案例的附录）

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)