The authors' official PyTorch SigCWGAN implementation.
This repository is the official implementation of [Conditional Sig-Wasserstein GANs for Time Series Generation]
To setup the conda enviroment:
conda env create -f requirements.yml
This repository contains implementations of synthetic and empirical datasets.
- Vector autoregressive (VAR) data
- Autoregressive conditionally heteroscedastic (ARCH)
- Real-world data:
- Stock data: https://realized.oxford-man.ox.ac.uk/data
We compare our SigCGAN with several baselines including: TimeGAN, RCGAN, GMMN(GAN with MMD). The baselines functions are in sig_lib/baselines.py
To reproduce the numerical results in the paper, save weights and produce a training summaries, run the following line:
python train.py -use_cuda -total_steps 1000
Optionally drop the flag
-use_cuda to run the experiments on CPU.
To evaluate models on different metrics and GPU, run:
python evaluate.py -use_cuda
As above, optionally drop the flag
-use_cuda to run the evaluation on CPU.
The numerical results will be saved in the 'numerical_results' folder during training process. Running evaluate.py will produce the 'summary.csv' files.