Generative adversarial networks (GANs) are the new architecture of neural networks, in which two models are trained simultaneously and their adversarial relationship (playing min-max game against each other) helps producing better results on set tasks.
Although this framework is still pretty new, it showed its potential on tasks like generating quality image or understanding features from images.
Time series represents a series of data points in time order, which contains multi-level information about the domain. Weather data is perfect example of such data, containing multiple levels of data information.
In this work, we will be utilizing GANs architecture to implement a method whose main purpose is to forecast weather. We will try to fully take advantage of all variations of this architecture, which could help the neural network to understand the problems of weather domain and thanks to that, generate forecasting from the past data.