The application and investigation of dropout layer in the generator of GAN in stock prediction

Recent years have witnessed rapid advancements in neural network capabilities. Among the cutting-edge techniques emerging in this field is the Generative Adversarial Network (GAN).The GAN framework comprises two competing neural networks: one aims to introduce noise and deceive its counterpart, while the other hones its skills on genuine data, guiding the first on enhancing the realism of its noise. After extensive training, the goal is for the former network to produce data so convincing that the latter cannot differentiate between authentic and fabricated. The studys objective is to harness this formidable technique to model time series data, with a primary focus on stock market dynamics. A GAN proficient in handling the complexities of time series data, notably unpredictable realms like the stock market, has vast potential applications. While GANs have proven adept at navigating the intricacies of time series data, they arent without challenges, notably the issue of overfitting. In this study, it will address this limitation by integrating a dropout layer into the generator. Such advancements in GAN could revolutionize the finance sector, offering improved risk evaluations for investments. Furthermore, GANs might hold the key to creating synthetic duplicates of confidential data, ensuring data veracity without jeopardizing confidentiality. In an era where vast amounts of data are locked away due to privacy concerns, the ability to generate precise, synthetic datasets could truly be groundbreaking.