# Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN Source: [Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:02:25.123Z Authors: Alireza Koochali, Peter Schichtel, Sheraz Ahmed, Andreas Dengel Published: 2019-03-29T14:39:00.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Probabilistic Forecasting on Mackey-Glass dataset (test)](): Probabilistic Forecasting; Mackey-Glass dataset (test); RMSE: 0.000382. - [Probabilistic Forecasting on Lorenz dataset (test)](): Probabilistic Forecasting; Lorenz dataset (test); RMSE: 4.06. - [Probabilistic Forecasting on Internet traffic dataset A5M (test)](): Probabilistic Forecasting; Internet traffic dataset A5M (test); MAPE: 2.94. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()