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AEC-GAN

This repository contains the code of AEC-GAN: Adversarial Error Correction GANs for Auto-Regressive Long Time-Series Generation.

Getting Started:

  1. Prepare the data:
    • We have provide the data in the folder data, containing the following six datasets:
      • [etth1, etth2, ettm1, ettm2, us_births, ILI]
  2. Install dependencies:
    • This project is implemented with pytorch==1.8.1+cu102
  3. For training 
    python train.py -datasets $dataset -base_dir $save_path -p $p -q $q -use_cuda -algos 'AECGAN' -total_steps 10000 -batch_size 200 -noise_type min_adv -use_ec 2
    • $dataset: We have implemented AEC-GAN on six datasets: [etth1, etth2, ettm1, ettm2, us_births, ILI]
    • $save_path: The path you save the model.
    • $p: The length of the past conditions.
    • $q: The length of the forward generations.
  4. For generation 
    python train.py -datasets $dataset -base_dir 'results/p168_q336' -p 168 -q 336 -use_cuda -algos 'AECGAN' -total_steps 10000 -batch_size 200 -noise_type min_adv -use_ec 2 -test
    • $dataset: We have implemented AEC-GAN on six datasets: [etth1, etth2, ettm1, ettm2, us_births, ILI]
    • $save_path: The path you save the model.
    • $p: The length of the past conditions.
    • $q: The length of the forward generations.
  5. For downstream performance
    • The generated data can be used as the alternative training set used for training forecasting models (e.g., SCINet, Informer and Autoformer).
    • Folder models contains the code to train the downstream forecasting models.
  6. Easy usage
    • For an easy usage, we also provide a bash file run_file.sh, which contains the commands to train the models or generate time-series data.

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