Fully Convolutional Network (FCN) was proposed in a paper [1]. FCN's architecture is shown in the figure below.

This repository offers a supervised-training code of FCN on all the subset datasets of the UCR archive. The datasets are automatically downloaded when relevant codes are executed. They, also, can be manually downloaded here. The trained FCN will be used to compute the FID (Fréchet Inception Distance) score for evaluation of generated time series.

The differences from its former version, supervised-FCN, are the followings:

• training and test datasets are re-created by merging the original training and test datasets and splitting it into 80% and 20% for a new training and test set with StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=0).
• the model is trained on the new training and test set.
• a larger size of the model is used. Instead of the hidden sizes of (128, 256, 128), (128, 256, 256, 256, 256, 128) is now used with Dropout layers in between.

This repository is a part of our paper "...".

pip install supervised-fcn-2

from _supervised_FCN_2_.example_pretrained_model_loading import load_pretrained_FCN

subset_dataset_name = ...  # 'Adiac'
fcn = load_pretrained_FCN(subset_dataset_name)
fcn.eval()

You can do the forward propagation as follows:

x = torch.rand((1, 1, 176))  # (batch_size, in_channels, length)
out = fcn(x)  # (batch_size, n_classes); an output right before the softmax layer.

You can obtain the representation (feature) vector (i.e., a vector right after the last pooling layer) as follows:

x = torch.rand((1, 1, 176))  # (batch_size, in_channels, length)
z = fcn(x, return_feature_vector=True)  # (batch_size, feature_dim)

from supervised_FCN_2.example_compute_FID import calculate_fid

x_real = torch.rand((1, 1, 176))  # (batch_size, in_channels, length)
x_fake = torch.rand((1, 1, 176))  # (batch_size, in_channels, length)

z_real = fcn(x_real, return_feature_vector=True)  # (batch_size, feature_dim)
z_fake = fcn(x_fake, return_feature_vector=True)  # (batch_size, feature_dim)

fid_score = calculate_fid(z_real.cpu().detach().numpy(), z_fake.cpu().detach().numpy())

from supervised_FCN_2.example_compute_IS import calculate_inception_score

x = torch.rand((1, 1, 176))  # (batch_size, in_channels, length)
out = fcn(x)  # (batch_size, n_classes); an output right before the softmax layer.
p_yx = torch.nn.functional.softmax(out, dim=-1)  # p(y|x); (batch_size, n_classes)

IS_mean, IS_std = calculate_inception_score(p_yx.cpu().detach().numpy())

You need to download the UCR archive dataset and put it in supervised_FCN/datasets/. You can download it from here. Then, your supervised_FCN/datasets directory should have supervised_FCN/datasets/UCRArchive_2018.

supervised_FCN/train_fcn.py: runs training of FCN on a subset dataset from the UCR archive.

supervised_FCN/configs/config.yaml: is where you can set parameters and hyper-parameters for dataset loading and training.

The training histories of FCN on all the UCR subset datasets are available here.

A training and test dataset split is the same as provided in the UCR archive, and a test dataset is used as a validation set during training for better training progress tracking.

• initial learning rate: 1e-3
• learning rate scheduler: cosine learning rate scheduler
• optimizer: AdamW
• weight decay: 1e-5
• max epochs: 1000
• batch size: 256

supervised_FCN/example_data_loading.py: a template for loading a subset dataset from the UCR archive.

supervised_FCN/example_pretrained_model_loading.py: a template for loading a pretrained FCN.

supervised_FCN/example_compute_FID.py: an example of computing the FID score.

supervised_FCN/example_compute_IS.py: an example of computing the IS (Inception Score).

[1] Wang, Zhiguang, Weizhong Yan, and Tim Oates. "Time series classification from scratch with deep neural networks: A strong baseline." 2017 International joint conference on neural networks (IJCNN). IEEE, 2017.

@article{lee2023vector,
  title={Vector Quantized Time Series Generation with a Bidirectional Prior Model},
  author={Lee, Daesoo and Malacarne, Sara and Aune, Erlend},
  journal={arXiv preprint arXiv:2303.04743},
  year={2023}
}