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 needed 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

Documentation

The documentation of the model can be found in https://s-tsfe-dl.readthedocs.io/en/latest/

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

To run an example, navigate to any file and run it.

```bash cd project/examples

run example

python arrythmia_experiment.py ```

Imports

This project is set up 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(inputtensor=input, includetop=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/)

@article{AGUILERAMARTOS2023223, title = {{TSFEDL}: A python library for time series spatio-temporal feature extraction and prediction using deep learning}, journal = {Neurocomputing}, volume = {517}, pages = {223-228}, year = {2023}, doi = {https://doi.org/10.1016/j.neucom.2022.10.062}, author = {Ignacio Aguilera-Martos and Ángel M. García-Vico and Julián Luengo and Sergio Damas and Francisco J. Melero and José Javier Valle-Alonso and Francisco Herrera} }

ArXiV reference with extended material: https://arxiv.org/abs/2206.03179