TSFEDL 1.0.5

Time Series Spatio-Temporal Feature Extraction using Deep Learning

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

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.

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.

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.

pip3 install --use-feature=2020-resolver tsfedl

Otherwise use

pip3 install tsfedl

PyPi

The module is uploaded to PyPi for an easy installation:

pip install tsfedl

or

pip3 install tsfedl

Using the repository

First, install dependencies

# 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.

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:

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/)

Paper citation is pending.