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Proceeding Paper

If You Like It, GAN It—Probabilistic Multivariate Times Series Forecast with GAN †

by
Alireza Koochali
1,2,3,*,
Andreas Dengel
2,3 and
Sheraz Ahmed
2
1
Ingenieurgesellschaft Auto und Verkehr (IAV) GmbH, 10587 Berlin, Germany
2
Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) GmbH, 67663 Kaiserslautern, Germany
3
Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany
*
Author to whom correspondence should be addressed.
†
Presented at the 7th International Conference on Time Series and Forecasting, Gran Canaria, Spain, 19–21 July 2021.
Eng. Proc. 2021, 5(1), 40; https://doi.org/10.3390/engproc2021005040
Published: 8 July 2021
(This article belongs to the Proceedings of The 7th International Conference on Time Series and Forecasting)
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Abstract

The contribution of this paper is two-fold. First, we present ProbCast—a novel probabilistic model for multivariate time-series forecasting. We employ a conditional GAN framework to train our model with adversarial training. Second, we propose a framework that lets us transform a deterministic model into a probabilistic one with improved performance. The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN’s component carefully and efficiently. We conduct experiments over two publicly available datasets—an electricity consumption dataset and an exchange-rate dataset. The results of the experiments demonstrate the remarkable performance of our model as well as the successful application of our proposed framework.
Keywords: time-series; generative adversarial networks; forecasting; probabilistic; prediction time-series; generative adversarial networks; forecasting; probabilistic; prediction

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MDPI and ACS Style

Koochali, A.; Dengel, A.; Ahmed, S. If You Like It, GAN It—Probabilistic Multivariate Times Series Forecast with GAN. Eng. Proc. 2021, 5, 40. https://doi.org/10.3390/engproc2021005040

AMA Style

Koochali A, Dengel A, Ahmed S. If You Like It, GAN It—Probabilistic Multivariate Times Series Forecast with GAN. Engineering Proceedings. 2021; 5(1):40. https://doi.org/10.3390/engproc2021005040

Chicago/Turabian Style

Koochali, Alireza, Andreas Dengel, and Sheraz Ahmed. 2021. "If You Like It, GAN It—Probabilistic Multivariate Times Series Forecast with GAN" Engineering Proceedings 5, no. 1: 40. https://doi.org/10.3390/engproc2021005040

APA Style

Koochali, A., Dengel, A., & Ahmed, S. (2021). If You Like It, GAN It—Probabilistic Multivariate Times Series Forecast with GAN. Engineering Proceedings, 5(1), 40. https://doi.org/10.3390/engproc2021005040

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