# CSL

**Repository Path**: shenaoHUST/CSL

## Basic Information

- **Project Name**: CSL
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-06
- **Last Updated**: 2025-03-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Official Implementation of the Contrastive Shapelet Learning (CSL) Approach for General-purpose Unsupervised (Self-supervised) Multivariate Time Series Representation Learning

## Requirements
* Python3.x
* Pytorch
* Numpy
* Sklearn
* tslearn
* tsaug

## Datasets
We use the 30 datasets from UEA archive and four anomaly detection datasets in this study.

* [UEA Archive](http://www.timeseriesclassification.com/)

* [Soil Moisture Active Passive satellite (SMAP)
and Mars Science Laboratory rover (SML) datasets](https://dl.acm.org/doi/10.1145/3219819.3219845)

* [Server Machine
Dataset, SMD](https://dl.acm.org/doi/10.1145/3292500.3330672)

* [Application Server Dataset, ASD](https://dl.acm.org/doi/10.1145/3447548.3467075)

The UEA datasets should be in the "Multivariate_ts/" folder with the structure `Multivariate_ts/[dataset_name]/[dataset_name]_TRAIN.ts` and `Multivariate_ts/[dataset_name]/[dataset_name]_TEST.ts`.

For SMAP and MSL datasets, create a folder named `SMAP&MSL` under 'AD_data/', and put the `.npy` data files into `AD_data/SMAP&MSL/`.

Similarly, to test SMD and ASD datasets, create a folder named `SMD&ASD` under 'AD_data/' then put the data files of `.pkl` into the folder `AD_data/SMD&ASD/`.


## Usage

To evaluate the UEA datasets using the commands:

Classification:

`python UEA.py [dataset_name]`

Clustering:

`python UEA.py [dataset_name] --task clustering`

For anomaly detection, use the following command:

`python CSL_AD.py [dataset_name] --window-size [window-size]`

Use `-h` or `--help` option for the detailed messages of the other options, such as the hyper-parameters and the random seed.