20.500.12556/RUL-165331 Improving stochastic models by smart denoising and latent representation optimization This paper introduces an innovative deep learning-based optimization method specifically designed for data derived from stochastic processes. Addressing the prevalent issue of rapid overfitting in real-world scenarios with limited historical data, our approach focuses on denoising optimization. The method effectively balances the simultaneous optimization of latent data representation and target variables, leading to enhanced model performance. We rigorously test our approach using five diverse real-world datasets. Our study is structured into three parts: an ablation study to validate the individual components of our method, a statistical analysis using the Wilcoxon rank-sum test to confirm the superiority of our method against five research hypotheses, and a detailed exploration of parameter visualization and fine-tuning. The comprehensive evaluation demonstrates that our method not only outperforms existing techniques but also significantly contributes to the advancement of deep learning models for stochastic processes. The findings underscore the potential of our method as a robust solution to the challenges in modeling stochastic processes with deep learning, offering new avenues for efficient and accurate predictions. deep learning optimization stochastic processes umetna inteligenca globoko učenje stohastične metode true false true Angleški jezik Slovenski jezik Članek v reviji 2024-12-02 13:58:52 2024-12-02 13:58:57 2024-12-03 04:01:00 0000-00-00 00:00:00 2025 0 0 15 str. art. 121672 Vol. 692 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8 0020-0255 10.1016/j.ins.2024.121672 216522499 1-s2.0-S002002552401586X-main.pdf 1-s2.0-S002002552401586X-main.pdf 1 C540ED17ED4B9495F0332B9B845CD281 d592acb1620675504c971eb9b4597a7a1bc8e7f66401e7da3b33787620273b1e fc42f6a0-b0ac-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=196436 https://www.sciencedirect.com/science/article/pii/S002002552401586X 0 e9209f65-b0ac-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=196435 Fakulteta za matematiko in fiziko 0 0 0