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Academic Journal of Business & Management, 2021, 3(6); doi: 10.25236/AJBM.2021.030607.

SVR Model Used for Economic Fluctuation Analysis


Jiarui Wang1, Shanshan Hou2, Xuan Cheng3, Ke Fan4, Yingfa Zhang5, Ruiying Chen6

Corresponding Author:
Jiarui Wang

1Ningbo University, Ningbo City, Zhejiang Province, China

2Pegasus California School, Qingdao City, Shandong Province, China 

3Guanghua Cambridge International School AS, Shanghai, China

4University of California, CA, USA

5Australian National University, ACT, AUS

6Zhejiang University of Finance and Economics Dongfang College, Jiaxing City, Zhejiang Province, China


The purpose of this study is to find an optimal algorithm for the prediction of market value and the analysis of economic fluctuations. We propose an ensemble learning algorithm based on SVR and apply it to market value prediction and economic fluctuation analysis. It was found that in most situations, the smaller the window value of short-term learning model is, the smaller the weight of long-term learning model is, and the better the performance of ensemble learning model is. However, with the decrease of weight value, ensemble learning model will have the problem of over-fitting, which makes the performance of the model decline. This paper proposes a market value forecasting model based on long-term and short-term ensemble learning. In the theory of SVR model, the validity and superiority of the model are verified through a large number of experiments. [1]


SVR Model, Economic Fluctuation, long-term, short-term

Cite This Paper

Jiarui Wang, Shanshan Hou, Xuan Cheng, Ke Fan, Yingfa Zhang, Ruiying Chen. SVR Model Used for Economic Fluctuation Analysis. Academic Journal of Business & Management (2021) Vol. 3, Issue 6: 59-62. https://doi.org/10.25236/AJBM.2021.030607.


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[3] Huang, Wei, Yoshiteru Nakamori, and Shou-Yang Wang. "Forecasting stock market movement direction with support vector machine." Computers & operations research 32.10 (2005): 2513-2522. 

[4] Trafalis, Theodore B., and Huseyin Ince. "Benders decomposition technique for support vector regression." Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No. 02CH37290). Vol. 3. IEEE, 2002.

[5] Mei, Wenjuan, et al. "Stock price prediction based on ARIMA-SVM model." (2018).