Time-Series prediction for the epidemic trends of COVID-19 using Conditional Generative adversarial Networks Regression on country-wise case studies

Abstract Probabilistic Regression is a statistical technique and a crucial problem in the machine learning domain which employs a set of machine learning methods to forecast a continuous target variable based on the value of one or multiple predictor variables. COVID-19 is a virulent virus that has brought the whole world to a standstill. The potential of the virus to cause inter human transmission makes the world a dangerous place. This thesis predicts the upcoming circumstances of the Corona virus to subside its action. We have performed Conditional GAN regression to anticipate the subsequent Covid-19 cases of 5 countries. The GAN variant CGAN is used to design the model and predict the Covid-19 cases for three months ahead with least error for the dataset provided. Each country is examined individually, due to their variation in population size, tradition, medical manage- ment, preventive measures. The analysis is based on confirmed data, as provided by the World Health Organization. This paper investigates how conditional Generative Adversarial Networks (GANs) can be used to accurately exhibit intricate conditional distributions. GANs have got spectacular achievement in producing convoluted highdimensional data, but work done on their use for regression prob- lems is minimal. This paper exhibits how conditional GANs can be employed in probabilistic regression. It is shown that conditional GANs can be used to evaluate a wide range of various distributions and be competitive with existing probabilistic regression models.