Xiaochao Xing1, Mingyu Wan1, and Qi Chu This email address is being protected from spambots. You need JavaScript enabled to view it.1

1College of Mathematics and Systems Science, Shandong University of Science and Technology, 266590, Qingdao, China


Received: March 11, 2022
Accepted: April 24, 2022
Publication Date: September 11, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202306_26(6).0003  


The imbalanced regional financial development usually hinders the healthy development of the national economy. In the past, scholars focused on the study of indicators affecting financial development and put forward suggestions to improve financial development, but few proposed the idea of predicting the future financial development level of a certain region through integrated learning. This paper aims to solve this problem by constructing regional financial development system. Taking Beijing as an example, the author firstly constructs the index system of regional financial development, and uses principal component analysis method to reduce the dimension of 41 financial indexes. Then, the index system is coupled with the attribute hierarchy model and the importance of criteria through the inter-criteria correlation algorithm. Finally, Bagging weighted ensemble learning is used to integrate artificial neural network model, multivariable linear regression model and Holt-Winters prediction algorithm to predict the future financial development level of the region with high precision. Calculate the financial development index of Beijing from 2021 to 2025, and the financial development index of Beijing in 2025 reaches 9.891. Through empirical analysis, this paper verifies the practicality of the ANN-MLR-Holt-Winters integrated learning model, which can not only predict the data of future indicators and their proportion in the overall level of financial development, but also help departments predict the future of some indicators.

Keywords: AHM-CRITIC; Bagging integrated learning; Regional financial development


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