95 citations · 117 across the 7 of their papers we have counts for
7 papers
VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting
Alireza Koochali, Ensiye Tahaei, Andreas Dengel +1
This paper presents VAEneu, an innovative autoregressive method for multistep ahead univariate probabilistic time series forecasting. We employ the conditional VAE framework and op…
Class Conditional Time Series Generation with Structured Noise Space GAN
Hamidreza Gholamrezaei, Alireza Koochali, Andreas Dengel +1
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 imag…
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction
Amin E. Bakhshipour, Alireza Koochali, Ulrich Dittmer +3
Despite various breakthroughs in machine learning and data analysis techniques for improving smart operation and management of urban water infrastructures, some key limitations obs…
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain
Alireza Koochali, Maria Walch, Sankrutyayan Thota +3
Generative models are designed to address the data scarcity problem. Even with the exploding amount of data, due to computational advancements, some applications (e.g., health care…
Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability
Alireza Koochali, Peter Schichtel, Andreas Dengel +1
The recent developments in the machine learning domain have enabled the development of complex multivariate probabilistic forecasting models. Therefore, it is pivotal to have a pre…
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN
Alireza Koochali, Andreas Dengel, Sheraz Ahmed
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…
Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
Alireza Koochali, Peter Schichtel, Sheraz Ahmed +1
Time series forecasting is one of the challenging problems for humankind. Traditional forecasting methods using mean regression models have severe shortcomings in reflecting real-w…