Research map / Time series / Causal and generative time series
AEC-GAN: Adversarial Error Correction GANs for Auto-Regressive Long Time-Series Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the error correction module to refine generated conditions during autoregressive generation (ablation) | AEC-GAN · Long sequence generation | FD | Helped |
| Add adversarial-example augmentation to GAN training (ablation) | AEC-GAN · Long sequence generation | FD | Helped |
| Add adversarial-example augmentation to GAN training (ablation) | AEC-GAN · ILI | gradient norm | Helped |
| Use AEC-GAN rather than the strongest listed baseline | Time-series GANs · ETTh1, length 4000 | FD: 5.606 -> 0.752 | Helped |
| Use AEC-GAN rather than the strongest listed baseline | Time-series GANs · US Births, length 4000 | FD: 6.302 -> 0.447 | Helped |
| Use AEC-GAN rather than the strongest listed baseline | Time-series GANs · ILI, length 4000 | FD: 2.157 -> 2.082 | Helped |
| Use AEC-GAN rather than the strongest listed baseline | Time-series GANs · ILI | ACF MSE: 0.312 -> 0.329 | Hurt |
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