Deep-temporal-clustering pytorch implemention

A non-official pytorch implementation of the DTC model , presented in the paper :

Madiraju, N. S., Sadat, S. M., Fisher, D., & Karimabadi, H. (2018). Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features. http://arxiv.org/abs/1802.01059

This an unsupervised architecture for the classification of multivariate time series.

Usage

To train the model , you can run the following command :

$ python3 train.py --similarity --pool

Note that the similarity and pool arguments are required. To see the full list of arguments , including the dataset name, please refer to the config.py file.

The autoencoder and clustering models weights will be saved in a models_weights directory. Also the train.py file returns the ROC score corresponding to the training parameters.

Further improvements

• Add heatmap network
• Add Auto Correlation based Similarity.
• Output more metrics for training.