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

• Soil Moisture Active Passive satellite (SMAP) and Mars Science Laboratory rover (SML) datasets

• Server Machine Dataset, SMD

• Application Server Dataset, ASD

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.