https://github.com/jsyoon0823/TimeGAN

Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019
https://github.com/jsyoon0823/TimeGAN

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Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019

• Host: GitHub
• URL: https://github.com/jsyoon0823/TimeGAN
• Owner: jsyoon0823
• License: other
• Created: 2020-04-24T21:32:32.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-07-30T21:00:20.000Z (4 months ago)
• Last Synced: 2024-08-02T14:09:05.829Z (4 months ago)
• Language: Jupyter Notebook
• Size: 1.25 MB
• Stars: 814
• Watchers: 9
• Forks: 258
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: license

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README

        
# Codebase for "Time-series Generative Adversarial Networks (TimeGAN)"

Authors: Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar

Reference: Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar, 

"Time-series Generative Adversarial Networks," 

Neural Information Processing Systems (NeurIPS), 2019.

 

Paper Link: https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks

Contact: [email protected]

This directory contains implementations of TimeGAN framework for synthetic time-series data generation

using one synthetic dataset and two real-world datasets.

-   Sine data: Synthetic

-   Stock data: https://finance.yahoo.com/quote/GOOG/history?p=GOOG

-   Energy data: http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction

To run the pipeline for training and evaluation on TimeGAN framwork, simply run 

python3 -m main_timegan.py or see jupyter-notebook tutorial of TimeGAN in tutorial_timegan.ipynb.

Note that any model architecture can be used as the generator and 

discriminator model such as RNNs or Transformers. 

### Code explanation

(1) data_loading.py

- Transform raw time-series data to preprocessed time-series data (Googld data)

- Generate Sine data

(2) Metrics directory

  (a) visualization_metrics.py

  - PCA and t-SNE analysis between Original data and Synthetic data

  (b) discriminative_metrics.py

  - Use Post-hoc RNN to classify Original data and Synthetic data

  (c) predictive_metrics.py

  - Use Post-hoc RNN to predict one-step ahead (last feature)

(3) timegan.py

- Use original time-series data as training set to generater synthetic time-series data

(4) main_timegan.py

- Report discriminative and predictive scores for the dataset and t-SNE and PCA analysis

(5) utils.py

- Some utility functions for metrics and timeGAN.

### Command inputs:

-   data_name: sine, stock, or energy

-   seq_len: sequence length

-   module: gru, lstm, or lstmLN

-   hidden_dim: hidden dimensions

-   num_layers: number of layers

-   iterations: number of training iterations

-   batch_size: the number of samples in each batch

-   metric_iterations: number of iterations for metric computation

Note that network parameters should be optimized for different datasets.

### Example command

```shell

$ python3 main_timegan.py --data_name stock --seq_len 24 --module gru

--hidden_dim 24 --num_layer 3 --iteration 50000 --batch_size 128 

--metric_iteration 10

```

### Outputs

-   ori_data: original data

-   generated_data: generated synthetic data

-   metric_results: discriminative and predictive scores

-   visualization: PCA and tSNE analysis