Papers › Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
Alireza Koochali, Peter Schichtel, Sheraz Ahmed, Andreas Dengel
Time series forecasting is one of the challenging problems for humankind. Traditional forecasting methods using mean regression models have severe shortcomings in reflecting real-world fluctuations. While new probabilistic methods rush to rescue, they fight with technical difficulties like quantile crossing or selecting a prior distribution. To meld the different strengths of these fields while avoiding their weaknesses as well as to push the boundary of the state-of-the-art, we introduce ForGAN - one step ahead probabilistic forecasting with generative adversarial networks. ForGAN utilizes the power of the conditional generative adversarial network to learn the data generating distribution and compute probabilistic forecasts from it. We argue how to evaluate ForGAN in opposition to regression methods. To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it. This dataset will be made publicly available for comparison. Furthermore, we test ForGAN on two publicly available datasets, namely Mackey-Glass dataset and Internet traffic dataset (A5M) where the impressive performance of ForGAN demonstrate its high capability in forecasting future values.
Code
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Tasks
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Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Probabilistic Time Series Forecasting | Internet Traffic dataset (A5M) | ForGAN | CRPS | 6.84e7 | #1 of 1 | Archive leaderboard | report |
| Probabilistic Time Series Forecasting | Internet Traffic dataset (A5M) | ForGAN | KLD | 2.84e-11 | #1 of 1 | Archive leaderboard | report |
| Probabilistic Time Series Forecasting | Lorenz dataset | ForGAN | CRPS | 1.511 | #1 of 1 | Archive leaderboard | report |
| Probabilistic Time Series Forecasting | Lorenz dataset | ForGAN | KLD | 1.67e-2 | #1 of 1 | Archive leaderboard | report |
| Probabilistic Time Series Forecasting | Mackey-Glass dataset | ForGAN | CRPS | 1.91e-4 | #1 of 1 | Archive leaderboard | report |
| Probabilistic Time Series Forecasting | Mackey-Glass dataset | ForGAN | KLD | 3.18e-3 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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