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

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Advances in Human Factors, Business Management and Leadership (AHFE 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1209))

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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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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430070, People’s Republic of China

    Chao Wang, Shengwu Xiong & Xiaoying Chen

  2. Hubei Credit Information Center, Wuhan, 430071, People’s Republic of China

    Shengwu Xiong

Authors
  1. Chao Wang
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  2. Shengwu Xiong
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  3. Xiaoying Chen
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Corresponding author

Correspondence to Chao Wang .

Editor information

Editors and Affiliations

  1. Department of Production, University of Vaasa, Vaasa, Finland

    Jussi Ilari Kantola

  2. Institute of Maritime Operations, University College of Southeast Norway, Borre, Norway

    Salman Nazir

  3. Häme University of Applied Sciences, Hämeenlinna, Finland

    Vesa Salminen

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Wang, C., Xiong, S., Chen, X. (2020). Correlation Situation Forecasting of Economic Indicators Based on Partial Least Squares and Kernel Method Regression Model. In: Kantola, J., Nazir, S., Salminen, V. (eds) Advances in Human Factors, Business Management and Leadership. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1209. Springer, Cham. https://doi.org/10.1007/978-3-030-50791-6_67

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50790-9

  • Online ISBN: 978-3-030-50791-6

  • eBook Packages: EngineeringEngineering (R0)

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