Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/alexandervnikitin/tsgm
Generation and evaluation of synthetic time series datasets (also, augmentations, visualizations, a collection of popular datasets)
https://github.com/alexandervnikitin/tsgm
augmentations data-augmentation data-science datasets deep-learning generative-model keras machine-learning python synthetic-data synthetic-time-series tensorflow2 time-series vae
Last synced: 24 days ago
JSON representation
Generation and evaluation of synthetic time series datasets (also, augmentations, visualizations, a collection of popular datasets)
- Host: GitHub
- URL: https://github.com/alexandervnikitin/tsgm
- Owner: AlexanderVNikitin
- License: apache-2.0
- Created: 2022-06-03T08:35:31.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-11T13:53:04.000Z (4 months ago)
- Last Synced: 2024-07-15T13:08:13.624Z (4 months ago)
- Topics: augmentations, data-augmentation, data-science, datasets, deep-learning, generative-model, keras, machine-learning, python, synthetic-data, synthetic-time-series, tensorflow2, time-series, vae
- Language: Python
- Homepage: https://tsgm.readthedocs.io
- Size: 9.79 MB
- Stars: 112
- Watchers: 4
- Forks: 15
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-time-series - tsgm
README
Time Series Generative Modeling (TSGM)
Create and evaluate synthetic time series datasets effortlessly
[](https://colab.research.google.com/drive/1l2VB6eUwvrxyu8iB30faGiQM5AKthc82?usp=sharing)
[](https://pypi.org/project/tsgm/)
[](https://github.com/AlexanderVNikitin/tsgm/actions?query=workflow%3ATests+branch%3Amain)
[](https://www.python.org/downloads/release/python-380/)
[](https://codecov.io/gh/AlexanderVNikitin/tsgm)
[](https://arxiv.org/abs/2305.11567)
Get Started •
Tutorials •
Augmentations •
Generators •
Metrics •
Datasets •
Contributing •
Citing
## :jigsaw: Get Started
TSGM is an open-source framework for synthetic time series dataset generation and evaluation.
The framework can be used for creating synthetic datasets (see :hammer: Generators ), augmenting time series data (see :art: Augmentations ), evaluating synthetic data with respect to consistency, privacy, downstream performance, and more (see :chart_with_upwards_trend: Metrics ), using common time series datasets (TSGM provides easy access to more than 140 datasets, see :floppy_disk: Datasets ).
We provide:
* [Documentation](https://tsgm.readthedocs.io/en/latest/) with a complete overview of the implemented methods,
* [Tutorials](https://github.com/AlexanderVNikitin/tsgm/tree/main/tutorials) that describe practical use-cases of the framework.
### Install TSGM
```bash
pip install tsgm
```
#### M1 and M2 chips:
To install `tsgm` on Apple M1 and M2 chips:
```bash
# Install tensorflow
conda install -c conda-forge tensorflow=2.9.1
# Install tsgm without dependencies
pip install tsgm --no-deps
# Install rest of the dependencies (separately here for clarity)
conda install tensorflow-probability scipy antropy statsmodels dtaidistance networkx optuna prettytable seaborn scikit-learn yfinance tqdm
```
### Train your generative model
```python
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)
```
## :anchor: Tutorials
- [](https://colab.research.google.com/drive/1l2VB6eUwvrxyu8iB30faGiQM5AKthc82?usp=sharing) Introductory Tutorial [Getting started with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb)
- [](https://colab.research.google.com/drive/1frSnJQSsuPS3asgIkmcrNtX4Y7TIQI56?usp=sharing) Tutorial [Datasets in TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Datasets.ipynb)
- [](https://colab.research.google.com/drive/1Vw9t4TlI1Nek_t6bMPyKcPPPqCiXfOK3?usp=sharing) Tutorial [Time Series Augmentations](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/augmentations.ipynb)
- [](https://colab.research.google.com/drive/1_jpGrPcwoSpB8eii8XW-spaikczdPqIQ?usp=sharing) Tutorial [Time Series Generation with VAEs](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/VAEs/VAE.ipynb)
- [](https://colab.research.google.com/drive/1rcN3pr8uglBEEOo4bTa1fvXgSMx1vKq9?usp=sharing) Tutorial [Conditional Time Series Generation with GANs](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb)
- [](https://colab.research.google.com/drive/1hubtddSX94KyLzuCTwmU6pAFBgBeiEB-?usp=sharing) Tutorial [Evaluation of Synthetic Time Series Data](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/evaluation.ipynb)
- [](https://colab.research.google.com/drive/1SKD9hRi-ic27Wts9Qzkssjfe1z7o1NU4?usp=sharing) Tutorial [Model Selection](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Model%20Selection.ipynb)
- [](https://colab.research.google.com/drive/1wpf9WeNVj5TkUcPF6EavVx-hUCOfyvUd?usp=sharing) Tutorial [Multiple GPUs or TPU with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Using%20Multiple%20GPUs%20or%20TPU.ipynb)
For more examples, see [our tutorials](./tutorials).
## :art: Augmentations
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](https://dl.acm.org/doi/pdf/10.1145/3136755.3136817) |
| Window Warping | `tsgm.augmentations.WindowWarping` | [Data Augmentation for Time Series Classification using Convolutional Neural Networks](https://shs.hal.science/halshs-01357973/document) |
| DTW Barycentric Averaging | `tsgm.augmentations.DTWBarycentricAveraging` | [A global averaging method for dynamic time warping, with applications to clustering.](https://www.sciencedirect.com/science/article/pii/S003132031000453X) |
## :hammer: Generators
TSGM implements several generative models for synthetic time series data.
| Method | Link to docs | Type | Notes |
| ------------- | ------------- | ------------- | ------------- |
| Structural Time Series | [sts.STS](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.sts.STS) | Data-driven | Great for modeling time series when prior knowledge is available (e.g., trend or seasonality). |
| GAN | [GAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.GAN) | Data-driven | A generic implementation of GAN for time series generation. It can be customized with architectures for generators and discriminators. |
| WaveGAN | [GAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.GAN) | Data-driven | WaveGAN is the model for audio synthesis proposed in [Adversarial Audio Synthesis](https://arxiv.org/abs/1802.04208). To use WaveGAN, set `use_wgan=True` when initializing the GAN class and use the `zoo["wavegan"]` architecture from the model zoo. |
| ConditionalGAN | [ConditionalGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.ConditionalGAN) | Data-driven | A generic implementation of conditional GAN. It supports scalar conditioning as well as temporal one. |
| BetaVAE | [BetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.BetaVAE) | Data-driven | A generic implementation of Beta VAE for TS. The loss function is customized to work well with multi-dimensional time series. |
| cBetaVAE | [cBetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.cBetaVAE) | Data-driven | Conditional version of BetaVAE. It supports temporal a scalar condiotioning.|
| TimeGAN | [TimeGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.timegan.TimeGAN) | Data-driven | TSGM implementation of TimeGAN from [paper](https://papers.nips.cc/paper_files/paper/2019/hash/c9efe5f26cd17ba6216bbe2a7d26d490-Abstract.html) |
| SineConstSimulator | [SineConstSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.SineConstSimulator) | Simulator-based | Simulator-based synthetic signal that switches between constant and periodics functions. |
| Lotka Volterra | [LotkaVolterraSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.LotkaVolterraSimulator) | Simulator-based | Simulator-based synthetic signal that switches between constant and periodics functions. |
| PdM Simulator | [PdMSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.PredictiveMaintenanceSimulator) | Simulator-based | Simulator of predictive maintenance with multiple pieces of equipment from [paper](https://arxiv.org/pdf/2206.11574) |
## :chart_with_upwards_trend: Metrics
TSGM implements many metrics for synthetic time series evaluation. Check Section 3 from [our paper for more detail on the evaluation of synthetic time series](https://arxiv.org/pdf/2305.11567).
| Metric | Link to docs | Type | Notes |
| ------------- | ------------- | ------------- | ------------- |
| Distance in the space of summary statistics | [tsgm.metrics.DistanceMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.DistanceMetric) | Distance | Calculates a set of summary statistics in the original and synthetic data, and measures the distance between those. |
| Maximum Mean Discrepancy (MMD) | [tsgm.metrics.MMDMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.MMDMetric) | Distance | This metric calculated MMD between real and synthetic samples |
| Discriminative Score | [tsgm.metrics.DiscriminativeMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.DiscriminativeMetric) | Distance | The DiscriminativeMetric measures the discriminative performance of a model in distinguishing between synthetic and real datasets. |
| Demographic Parity Score | [tsgm.metrics.DemographicParityMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.DemographicParityMetric) | Fairness | This metric assesses the difference in the distributions of a target variable among different groups in two datasets. Refer to [this paper](https://fairware.cs.umass.edu/papers/Verma.pdf) to learn more. |
| Predictive Parity Score | [tsgm.metrics.PredictiveParityMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.PredictiveParityMetric) | Fairness | This metric assesses the discrepancy in the predictive performance of a model among different groups in two datasets. Refer to [this paper](https://fairware.cs.umass.edu/papers/Verma.pdf) to learn more. |
| Privacy Membership Inference Attack Score | [tsgm.metrics.PrivacyMembershipInferenceMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.PrivacyMembershipInferenceMetric) | Privacy | The metric measures the possibility of membership inference attacks.|
| Spectral Entropy | [tsgm.metrics.EntropyMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.EntropyMetric) | Diversity | Calculates the spectral entropy of a dataset or tensor as a sum of individual entropies. |
| Shannon Entropy | [tsgm.metrics.ShannonEntropyMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.ShannonEntropyMetric) | Diversity | Shannon Entropy calculated over the labels of a dataset. |
| Pairwise Distance | [tsgm.metrics.PairwiseDistanceMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.PairwiseDistanceMetric) | Diversity | Measures pairwise distances in a set of time series. |
| Downstream Effectiveness | [tsgm.metrics.DownstreamPerformanceMetric](https://tsgm.readthedocs.io/en/latest/autoapi/tsgm/metrics/index.html#tsgm.metrics.DownstreamPerformanceMetric) | Downstream Effectiveness | The downstream performance metric evaluates the performance of a model on a downstream task. It returns performance gains achieved with the addition of synthetic data. |
| Qualitative Evaluation | [tsgm.utils.visualization](https://tsgm.readthedocs.io/en/latest/modules/root.html#module-tsgm.utils.visualization) | Qualitative | Various tools for visual assessment of a generated dataset. |
## :floppy_disk: Datasets
| Dataset | API | Description |
| - |---------------------------------------------------| ------------- |
| UCR Dataset | `tsgm.utils.UCRDataManager` | https://www.cs.ucr.edu/%7Eeamonn/time_series_data_2018/ |
| Mauna Loa | `tsgm.utils.get_mauna_loa()` | https://gml.noaa.gov/ccgg/trends/data.html |
| EEG & Eye state | `tsgm.utils.get_eeg()` | https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State |
| Power consumption dataset | `tsgm.utils.get_power_consumption()` | https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption |
| Stock data | `tsgm.utils.get_stock_data(ticker_name)` | Gets historical stock data from YFinance |
| COVID-19 over the US | `tsgm.utils.get_covid_19()` | Covid-19 distribution over the US |
| Energy Data (UCI) | `tsgm.utils.get_energy_data()` | https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction |
| MNIST as time series | `tsgm.utils.get_mnist_data()` | https://en.wikipedia.org/wiki/MNIST_database |
| Samples from GPs | `tsgm.utils.get_gp_samples_data()` | https://en.wikipedia.org/wiki/Gaussian_process |
| Physionet 2012 | `tsgm.utils.get_physionet2012()` | https://archive.physionet.org/pn3/challenge/2012/ |
| Synchronized Brainwave Dataset | `tsgm.utils.get_synchronized_brainwave_dataset()` | https://www.kaggle.com/datasets/berkeley-biosense/synchronized-brainwave-dataset |
TSGM provides API for convenient use of many time-series datasets (currently more than 140 datasets). The comprehensive list of the datasets in the [documentation](https://tsgm.readthedocs.io/en/latest/guides/datasets.html)
## :hammer_and_wrench: Contributing
We appreciate all contributions. To learn more, please check [CONTRIBUTING.md](CONTRIBUTING.md).
#### For contributors
```bash
git clone github.com/AlexanderVNikitin/tsgm
cd tsgm
pip install -e .
```
Run tests:
```bash
python -m pytest
```
To check static typing:
```bash
mypy
```
## :computer: CLI
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.
## :mag: Citing
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}
}
```
## License
[Apache License 2.0](LICENSE)