SVR Model Used for Economic Fluctuation Analysis

Jiarui Wang, Shanshan Hou, Xuan Cheng, K. Fan, Yingfa Zhang, Ruiying Chen
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Abstract

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]
用于经济波动分析的SVR模型
本研究的目的是寻找一种最优的算法来预测市场价值和分析经济波动。提出了一种基于支持向量回归的集成学习算法,并将其应用于市场价值预测和经济波动分析。研究发现,在大多数情况下,短期学习模型的窗口值越小,长期学习模型的权重越小,集成学习模型的性能越好。然而,随着权值的减小,集成学习模型会出现过拟合的问题,使模型的性能下降。提出了一种基于长期和短期集成学习的市场价值预测模型。在SVR模型理论中,通过大量的实验验证了该模型的有效性和优越性。[1]
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