TSGM is an open-source framework for synthetic time series generation and augmentation.
The framework can be used for:
- creating synthetic data, using historical data, black-box models, or a combined approach,
- augmenting time series data,
- evaluating synthetic data with respect to consistency, privacy, downstream performance, and more.
pip install tsgm
Introductory Tutorial "Getting started with TSGM"
For more examples, see our tutorials.
import tsgm
# ... Define hyperparameters ...
# dataset is a tensor of shape n_samples x seq_len x feature_dim
# Zoo contains several prebuilt architectures: we choose a conditional GAN architecture
architecture = tsgm.models.architectures.zoo["cgan_base_c4_l1"](
seq_len=seq_len, feat_dim=feature_dim,
latent_dim=latent_dim, output_dim=0)
discriminator, generator = architecture.discriminator, architecture.generator
# Initialize GAN object with selected discriminator and generator
gan = tsgm.models.cgan.GAN(
discriminator=discriminator, generator=generator, latent_dim=latent_dim
)
gan.compile(
d_optimizer=keras.optimizers.Adam(learning_rate=0.0003),
g_optimizer=keras.optimizers.Adam(learning_rate=0.0003),
loss_fn=keras.losses.BinaryCrossentropy(from_logits=True),
)
gan.fit(dataset, epochs=N_EPOCHS)
# Generate 100 synthetic samples
result = gan.generate(100)We provide:
- Documentation with a complete overview of the implemented methods,
- Tutorials that describe practical use-cases of the framework.
git clone github.com/AlexanderVNikitin/tsgm
cd tsgm
pip install -e .Run tests:
python -m pytestTo check static typing:
mypyWe provide two CLIs for convenient synthetic data generation:
tsgm-gdgenerates data by a stored sample,tsgm-evalevaluates the generated time series.
Use tsgm-gd --help or tsgm-eval --help for documentation.
TSGM provides API for convenient use of many time-series datasets (currently more than 15 datasets). The comprehensive list of the datasets in the documentation
TSGM provides a number of time series augmentations.
| Augmentation | Class in TSGM | Reference |
|---|---|---|
| Gaussian Noise / Jittering | tsgm.augmentations.GaussianNoise |
- |
| Slice-And-Shuffle | tsgm.augmentations.SliceAndShuffle |
- |
| Shuffle features | tsgm.augmentations.Shuffle |
- |
| Magnitude warping | tsgm.augmentations.MagnitudeWarping |
Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural Networks |
| Window warping | tsgm.augmentations.WindowWarping |
Data Augmentation for Time Series Classification using Convolutional Neural Networks |
We appreciate all contributions. To learn more, please check CONTRIBUTING.md.
If you find this repo useful, please consider citing our paper:
@article{
nikitin2023tsgm,
title={TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series},
author={Nikitin, Alexander and Iannucci, Letizia and Kaski, Samuel},
journal={arXiv preprint arXiv:2305.11567},
year={2023}
}