Metadata-Version: 2.1
Name: TSFEDL
Version: 1.0.6
Summary: Time Series Spatio-Temporal Feature Extraction using Deep Learning
Home-page: https://github.com/ari-dasci/S-TSFE-DL
Author: Ignacio Aguilera Martos, Ángel Miguel García Vico, Julian Luengo, Francisco Herrera
Author-email: nacheteam@ugr.es
License: UNKNOWN
Description: # TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning.
        
        ## Description
        
        Time series feature extraction is a classical problem in time series analysis. Classical addition and multiplication models have been used for this purpose until the appearance of Artificial Neural Networks and Deep Learning. This problem has gained attention since multiple real life problems imply the usage of time series.
        
        In this repository we introduce a new Python module which compiles 20 backbones for time series feature extraction using Deep Learning. This module has been created to cover the necessity of a versatile and expandable piece of software for practitioners to use in their problems.
        
        ## How to run
        
        ### Conda environment for GPU clusters
        
        To easily use the library inside a conda environment the following commands are recommended to install the module. First of all install pip inside anaconda, which will install python inside the environment as well to encapsulate the whole installation.
        
        ```bash
        conda install -c anaconda pip
        ```
        
        After this, if a GPU is going to be used, we should install cuDNN 8.2.1 for the current tensorflow-gpu version (2.6.0). The NVIDIA CUDA toolkit will be also installed as a cuDNN dependency.
        
        ```bash
        conda install -c anaconda cudnn==8.2.1
        ```
        
        Finally we can install the TSFEDL library using pip3 (which will be inside the conda environment, you can check this by running "which pip3"). This will install as dependencies pytorch-lightning, pytorch, tensorflow-gpu and all the needeed packages. Use the --use-feature=2020-resolver flag if the installation runs into an error.
        
        ```bash
        pip3 install --use-feature=2020-resolver tsfedl
        ```
        
        Otherwise use
        
        ```bash
        pip3 install tsfedl
        ```
        
        After this everything is set up.
        
        ### PyPi
        
        The module is uploaded to PyPi for an easy installation:
        ```bash
        pip install tsfedl
        ```
        or
        ```bash
        pip3 install tsfedl
        ```
        
        ### Using the repository
        
        First, install dependencies
        
        ```bash
        # clone project
        git clone https://github.com/ari-dasci/S-TSFE-DL.git
        
        # install project
        cd S-TSFE-DL
        pip install -e .
        ```   
        
        ### Examples
        
        In order to run a example, navigate to any file and run it.
        
        ```bash
        cd project/examples
        
        # run example
        python arrythmia_experiment.py
        ```
        
        ## Imports
        This project is setup as a package which means you can now easily import any file into any other file like so:
        
        ```python
        import tensorflow as tf
        import TSFEDL.models_keras as TSFEDL
        
        # get the OhShuLih model
        model = TSFEDL.OhShuLih(input_tensor=input, include_top=True)
        
        # compile and fit as usual
        model.compile(optimizer='Adam')
        model.fit(X, y, epochs=20)
        ```
        
        ## Citation
        
        Please cite this work as:
        
        Time Series Feature Extraction using Deep Learning library (https://github.com/ari-dasci/S-TSFE-DL/)
        
        ArXiV reference: https://arxiv.org/abs/2206.03179
        
        ```
        @article{https://doi.org/10.48550/arxiv.2206.03179,
          doi = {10.48550/ARXIV.2206.03179},
          url = {https://arxiv.org/abs/2206.03179},
          author = {Aguilera-Martos, Ignacio and García-Vico, Ángel M. and Luengo, Julián and Damas, Sergio and Melero, Francisco J. and Valle-Alonso, José Javier and Herrera, Francisco},
          keywords = {Neural and Evolutionary Computing (cs.NE), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
          title = {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)},
          publisher = {arXiv},
          year = {2022},
          copyright = {arXiv.org perpetual, non-exclusive license}
        }
        ```
        
Keywords: Time series,Feature extraction,Deep learning,recurrent,cnn
Platform: UNKNOWN
Description-Content-Type: text/markdown
