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Found 6 papers, 2 papers with code
Class Conditional Time Series Generation with Structured Noise Space GAN
This paper introduces Structured Noise Space GAN (SNS-GAN), a novel approach in the field of generative modeling specifically tailored for class-conditional generation in both image and time series data.
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction
In this study, we developed a GAN model to generate synthetic time series to balance our limited recorded time series data and improve the accuracy of a data-driven model for combined sewer flow prediction.
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain
Generative models are designed to address the data scarcity problem.
Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability
The recent developments in the machine learning domain have enabled the development of complex multivariate probabilistic forecasting models.
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN
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.
Multivariate Time Series Forecasting
Probabilistic Time Series Forecasting
+1
Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it.
Generative Adversarial Network
Probabilistic Time Series Forecasting
+3
