Unofficial implementation of TimeGAN (Yoon et al., NIPS 2019) in PyTorch 2.
Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar, "Time-series Generative Adversarial Networks," Neural Information Processing Systems (NeurIPS), 2019
Original Codebase: https://github.com/jsyoon0823/TimeGAN.git
- Stock data: https://finance.yahoo.com/quote/GOOG/history?p=GOOG
- Energy data: http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction
The model was implemented and tested using Python==3.11.9. Further, the following modules were utilized (see Requirements File):
matplotlib==3.10.0
numpy==2.2.1
pandas==2.2.3
scikit-learn==1.6.0
torch==2.5.1
tqdm==4.67.1
To conduct the experiments, the easiest way to get started is by cloning this repository and use the notebook.
Alternatively, run it from the terminal.
py main_timegan.py --data=data/stock_data.csv --seq_len=24 --module=gru --hidden_dim=24 --num_layers=3 --epochs=10000 --batch_size=128 --metric_iteration=10 --learning_rate=1e-3- Visualization
