Documentation | Tutorials

TSGM is an open-source framework for synthetic time series generation.

The framework can be used for:

• creating synthetic data, using historical data, black-box models, or a combined approach,
• researching new approaches for synthetic data generation,
• comparison of different generative methods for time series.

We provide:

• Documentation where we give complete overview of the implemented methods,
• Tutorials with tutorials that describe practical use-cases of the framework.

git clone github.com/AlexanderVNikitin/tsgm
cd tsgm
make install

Run tests:

make test

To check static typing:

mypy

We provide two CLIs for convenient synthetic data generation:

• tsgm-gd generates data by a stored sample,
• tsgm-eval evaluates the generated time series.

Use tsgm-gd --help or tsgm-eval --help for documentation.

Here, we provide the gist of the framework, for the completed 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)

TSGM provides API for convenient use of many time-series datasets. The comprehensive list of the datasets in the documentation

We appreciate all contributions. To learn more, please check CONTRIBUTING.md.

Apache License 2.0