Academic Journal of Mathematical Sciences, 2024, 5(3); doi: 10.25236/AJMS.2024.050307.
Jiaru Wang1, Zhenhan Chen2
1School of Applied Mathematics, Shanxi University of Finance and Economics, Taiyuan, 030000, China
2School of Economics, Xinjiang University of Finance and Economics, Urumqi, 830000, China
The Gaussian process regression model is a flexible and powerful non -parameter regression method. It can fit the relatively complicated function of the Reproducing Kernel Hilbert Space (RKHS) by selecting the appropriate kernel function. However, the correctness of the selection of the kernel function has an important effect on the efficiency of the predictive model and there is no uniform paradigm for solution. Based on this, this article proposes a new self-adaptive Gaussian process regression model by using multiple kernel function. On the one hand, this proposed method can fit the predicted function in a more large RKHS, and adapts to solve the selection problem of kernel function. On the other hand, this method enhances the stability of the Gaussian process regression model by using the Bagging algorithm. In addition, the basis model in the Bagging framework is model-free. The simulation data analysis shows that the proposed model has smaller mean errors, relative errors and absolute errors relative to some comparison methods. Lastly, it is applied to the actual stock data of quantitative transactions and the results also reflect the accuracy and stability of the proposed model.
Stock Price Modeling, Gaussian Process Regression, Multi-Kernel, Bagging Algorithm
Jiaru Wang, Zhenhan Chen. Gaussian Process Regression Model Based on Multi-Kernel Learning and Bagging Algorithm. Academic Journal of Mathematical Sciences (2024) Vol. 5, Issue 3: 44-51. https://doi.org/10.25236/AJMS.2024.050307.
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