Work

Parameter Estimation in Multifactor Uncertain Differential Equation with Symmetry Analysis for Stock Prediction

Year: 2025

Type: article

Abstract: Multifactor uncertain differential equations (MUDEs) are effective tools to model dynamic systems under multi-source noise. With the widespread use of MUDEs, parameter estimation as the bridge between the observed data and the MUDE becomes increasingly important. Thus, how to estimate unknown parameters in a MUDE under a multi-source noise environment is a challenge. To address this, this paper innovatively proposes a moment method to estimate the unknown parameters in a MUDE and illustrates two numerical examples to show the process of estimating parameters. Furthermore, since the system or environment is complex and constantly changing, the parameters in the MUDE are not constants but time-varying functions in many cases. Therefore, parameter estimation for time-varying functions is another challenge. In order to deal with this, this paper develops a method of parameter estimation for time-varying functions in the MUDE based on the moment method. As an application, this method of parameter estimation for time-varying functions is used to model China Merchants Bank stock.

Source: Symmetry

DOI: 10.3390/sym17040620

Authors: Jiashuo Zhang, Tingqing Ye, Xiaoya Xu, Yang Liu, Haoran Zheng

Institutions: Shanghai University, China University of Geosciences (Beijing), Beihang University, People's Bank of China, Shanghai Jiao Tong University

Language: English

Topic: Fuzzy Systems and Optimization

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: China Postdoctoral Science Foundation