TSFEDL: A python']python library for time series spatio-temporal feature extraction and prediction using deep learning

被引：7

作者：

Aguilera-Martos, Ignacio ^{[1
,3
]}

Garcia-Vico, Angel M. ^{[1
,3
]}

Luengo, Julian ^{[1
,3
]}

Damas, Sergio ^{[2
,3
]}

Melero, Francisco J. ^{[2
,3
]}

Javier Valle-Alonso, Jose ^{[4
]}

Herrera, Francisco ^{[1
,3
]}

机构：

[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain

[2] Univ Granada, Dept Software Engn, Granada, Spain

[3] Andalusian Inst Data Sci & Computat Intelligence, Granada, Spain

[4] Repsol Technol Lab, Madrid, Spain

来源：

NEUROCOMPUTING | 2023年 / 517卷

关键词：

Time series; Deep learning; !text type='Python']Python[!/text; ARRHYTHMIA; NETWORK;

D O I：

10.1016/j.neucom.2022.10.062

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The combination of convolutional and recurrent neural networks is a promising framework. This arrangement allows the extraction of high-quality spatio-temporal features together with their temporal dependencies. This fact 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 22 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.(c) 2022 Elsevier B.V. All rights reserved.

引用

页码：223 / 228

页数：6

共 32 条

[1] Time series predicting of COVID-19 based on deep learning [J].