Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/okrasolar/pytorch-timeseries
PyTorch implementations of neural networks for timeseries classification
https://github.com/okrasolar/pytorch-timeseries
classification deep-learning machine-learning pytorch time-series
Last synced: 3 days ago
JSON representation
PyTorch implementations of neural networks for timeseries classification
- Host: GitHub
- URL: https://github.com/okrasolar/pytorch-timeseries
- Owner: okrasolar
- Created: 2019-09-28T07:24:23.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-01-27T19:08:46.000Z (almost 3 years ago)
- Last Synced: 2024-08-02T06:02:49.729Z (3 months ago)
- Topics: classification, deep-learning, machine-learning, pytorch, time-series
- Language: Python
- Homepage:
- Size: 53.7 KB
- Stars: 105
- Watchers: 3
- Forks: 23
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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](https://arxiv.org/abs/1909.04939)
* [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://arxiv.org/abs/1611.06455)### Beyond the UCR/UEA archive
There are two ways use the Inception Time model on your own data:1. Copy the [models](src/models), and write new training loops
2. Extend the [base trainer](src/trainer.py) 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`](src/ucr.py).### Setup
[Anaconda](https://www.anaconda.com/download/#macos) 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```bash
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```bash
conda activate inception
```In addition, [UCR/UEA archive](https://www.cs.ucr.edu/~eamonn/time_series_data/) must be downloaded and stored in the
[data folder](data).### Scripts
Example scripts showing how to train and evaluate the model can be found in the [scripts folder](scripts).