Improving stochastic models by smart denoising and latent representation optimization

Jelenčič, Jakob (author), Massri, M. Besher (author), Todorovski, Ljupčo (author), Grobelnik, Marko (author), Mladenić, Dunja (author)

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.

• Language: English
• Keywords: deep learning optimization, stochastic processes
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FMF - Faculty of Mathematics and Physics
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 15 str.
• Numbering: Vol. 692, art. 121672
• PID: 20.500.12556/RUL-165331
• UDC: 004.8
• ISSN on article: 0020-0255
• DOI: 10.1016/j.ins.2024.121672
• COBISS.SI-ID: 216522499
• Publication date in RUL: 02.12.2024

Record is a part of a journal

• Title: Information sciences
• Shortened title: Inf. sci.
• Publisher: Elsevier
• ISSN: 0020-0255
• COBISS.SI-ID: 25613056

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: umetna inteligenca, globoko učenje, stohastične metode

Projects

• Funder: ARRS - Slovenian Research Agency