If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN

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 namely electricity consumption dataset and 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.

References in corpus (6)

• Conditional Generative Adversarial Nets
• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
• Quant GANs: Deep Generation of Financial Time Series
• Conditional Transformation Models
• Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
• T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling

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