Improving Stochastic Models by Smart Denoising and Latent Representation Optimization

This paper proposes a novel optimization method for modeling stochastic processes using deep learning. The rapid overfitting of current methods poses a significant challenge, particularly in real-world applications with limited historical data. To address this issue, our method combines intelligent denoising with simultaneous optimization of latent data representation and target variables. Experiments conducted on two collections of real-world datasets demonstrate the effectiveness of our approach. We conduct a comprehensive parameter analysis and evaluate the results using the Wilcoxon page rank test. Our method outperforms existing techniques and contributes to the development of more efficient and accurate deep learning models for stochastic processes. By emphasizing the benefits of our approach, we aim to provide a promising solution to the challenging problem of modeling stochastic processes using deep learning.