Time Series Generative Modeling (TSGM) Official Documentation¶

Time Series Generative Modeling (TSGM) is a Python framework for time series data generation. It include data-driven and model-based approaches to synthetic time-series generation. It uses both generative

The package is built on top of Tensorflow that allows training the models on CPUs, GPUs, or TPUs.

Quick start:

pip install tsgm

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=1)

# Generate 10 synthetic samples
result = gan.generate(10)

For more examples, see our tutorials.

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}
}

Guides

Package Reference

TSGM