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Barriers to the Development of Regional Competitiveness in the Context of Regional Economies - EU, Latvia, Region Vidzeme Case Study Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Correlation Situation Forecasting of Economic Indicators Based on Partial Least Squares and Kernel Method Regression Model

Authors : Chao Wang, Shengwu Xiong, Xiaoying Chen

Published in: Advances in Human Factors, Business Management and Leadership

Publisher: Springer International Publishing

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Abstract

Accurate prediction of the development trend of various macroeconomic indicators can provide effective support for scientific government decision-making and accurate social governance. Based on the limitations of current macro-economic big data statistics, it is a formidable challenge to establish accurate and robust prediction models using small samples with high characteristic dimensions. Based on copula-based Granger analysis, we analyzed the relationship between macroeconomic indicators and extracted low-dimensional features of data by combining independent component analysis and partial least square method. On this basis, we further use the kernel function method to complete the virtual sample training set to train the support vector regression model to predict the macroeconomic indicators and obtain better experimental results.

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Metadata
Title
Correlation Situation Forecasting of Economic Indicators Based on Partial Least Squares and Kernel Method Regression Model
Authors
Chao Wang
Shengwu Xiong
Xiaoying Chen
Copyright Year
2020
Book
Advances in Human Factors, Business Management and Leadership

Print ISBN: 978-3-030-50790-9

Electronic ISBN: 978-3-030-50791-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-50791-6_67