# Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN

## TL;DR

This paper introduces ForGAN, a novel probabilistic forecasting method using generative adversarial networks to better capture real-world data fluctuations in time series predictions.

## Contribution

The paper presents ForGAN, a new approach that leverages GANs for probabilistic time series forecasting, overcoming limitations of traditional regression and probabilistic methods.

## Key findings

- ForGAN effectively models data distributions for forecasting.
- Demonstrates superior performance on Mackey-Glass and Internet traffic datasets.
- Provides a new dataset for benchmarking probabilistic forecasting methods.

## 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.