论文标题
TSFEDL：使用深度学习的时间序列时空特征提取和预测的Python库（附上有关详细网络体系结构和实验性研究案例的附录）
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)
论文作者
论文摘要
卷积和复发性神经网络的结合是一个有前途的框架，它允许提取高质量时空特征以及其时间依赖性，这是时间序列预测问题（例如预测，分类或异常检测）的关键。在本文中，引入了TSFEDL库。它通过使用卷积和经常性的深神经网络来编译时间序列提取和预测的20种最先进方法，用于在多个数据挖掘任务中使用。该库建在AGPLV3许可下的一组TensorFlow+Keras和Pytorch模块上。此提案中包含的体系结构的性能验证证实了此Python软件包的有用性。
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.
