cdsnlab/pytorch-timeseries

PyTorch implementations of neural networks for timeseries classification

pytorch-timeseries

PyTorch implementations of deep neural neural nets for time series classification.

Currently, the following papers are implemented:

• InceptionTime: Finding AlexNet for Time Series Classification
• Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

Beyond the UCR/UEA archive

There are two ways use the Inception Time model on your own data:

1. Copy the models, and write new training loops
2. Extend the base trainer by implementing an initializer, get_loaders and save. This allows the training code (which handles both single and multi-class outputs) to be used - an example of this is the UCRTrainer.

Setup

Anaconda running python 3.7 is used as the package manager. To get set up with an environment, install Anaconda from the link above, and (from this directory) run

conda env create -f environment.yml

This will create an environment named inception with all the necessary packages to run the code. To activate this environment, run

conda activate inception

In addition, UCR/UEA archive must be downloaded and stored in the data folder.

Scripts

Example scripts showing how to train and evaluate the model can be found in the scripts folder.