Title: Improvements and application of normal cloud positive regression model

Authors: Jiandong He; Xiaoting Tan; Chaolin Liu

Addresses: Jiaxing Vocational and Technical College, Jiaxing 314000, Zhejiang, China ' College of Mathematics and Statistics, Chongqing University, Chongqing City, China; School of Mathematics and Statistics, Wuhan University, Wuhan City, China ' College of Mathematics and Statistics, Chongqing University, Chongqing City, China; Provincial Demonstration Center for Experimental Mathematics Education, Chongqing University, Chongqing City, China

Abstract: The normal cloud model, as an important model for simultaneously studying the randomness and fuzziness of uncertainty problems, not only covers various disciplines and fields in practical applications but has also been promoted by scholars in theoretical research since its publication. This article introduces the normal cloud model and the cloud least squares method based on discrete envelope distance. In practical applications, the cloud least squares method may generate low accuracy or results that contradict the actual situation. To solve these problems, this article presents the improvements of the normal cloud linear regression model, which leads to the normal cloud ridge regression (NCR) and normal cloud positive regression models. Their advantages have been illustrated through simulation data analysis respectively. Finally, the model was applied to actual stock data to demonstrate the value of our modification once again.

Keywords: normal cloud model; normal cloud ridge regression model; cloud least squares method; normal cloud positive regression; NCP; mean square error.

DOI: 10.1504/IJDSDE.2025.146957

International Journal of Dynamical Systems and Differential Equations, 2025 Vol.14 No.1/2, pp.123 - 142

Received: 05 Mar 2024
Accepted: 02 Dec 2024
Published online: 27 Jun 2025 *

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