Financial Time Series Prediction Based on EMD-SVM

Kai Tang

doi:10.54691/bcpbm.v30i.2435

Authors

Kai Tang

DOI:

https://doi.org/10.54691/bcpbm.v30i.2435

Keywords:

EMD; SVM; Financial time series; Combined forecasting.

Abstract

The market plays an essential role in the national economy and society, and people pay more and more attention to the investment in the capital market. Every valuable investment needs the guidance of scientific theory. With the improvement of China's securities market system, more and more researchers have conducted in-depth research focusing on the development of the stock market. This paper will study the stock price prediction algorithm, select the Bank of Ningbo as the research object, and propose a prediction model based on the combination of EMD and SVM algorithms. This paper first decomposes the original sequence by EMD and comprehensively considers the three related factors of the Shanghai Composite Index, Shenzhen Composite Index, and China Merchants Bank in the prediction model. Then, the SVM model is used to predict multiple decomposition sequences, respectively, and the prediction results are obtained after integrated processing. Then, regression analysis is carried out on them, and the weight is designed according to the constructed regression analysis model. The final prediction result is obtained after reconstruction. Compared with the basic SVM model, the combined prediction model constructed in this paper has a better prediction effect, and the prediction results are more objective and scientific.

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