Financial Time Series Forecasting Using Support Vector Machine

The traditional financial time series forecasting methods use accurate input data for prediction, and then make single-step or multi-step prediction based on the established regression model. So its prediction result is one or more specific values. But because of the complexity of financial markets,...

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Bibliographic Details
Published in2014 Tenth International Conference on Computational Intelligence and Security pp. 39 - 43
Main Authors Bin Gui, Xianghe Wei, Qiong Shen, Jingshan Qi, Liqiang Guo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2014
Subjects
Educational institutions
financial time series
Forecasting
Fuzzy sets
Indexes
information granulation
regression
Reliability
support vector machine
Support vector machines
Time series analysis
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DOI10.1109/CIS.2014.22

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Summary:The traditional financial time series forecasting methods use accurate input data for prediction, and then make single-step or multi-step prediction based on the established regression model. So its prediction result is one or more specific values. But because of the complexity of financial markets, the traditional forecasting methods are less reliable. In this paper, we transform the financial time series into fuzzy grain particle sequences, and use support vector machine regression to regress the upper and lower bounds of the fuzzy particles, and then apply regression model single-step prediction on the upper and lower bounds, which will limit the predict results within a range. This is a new idea. The Shanghai Composite Index Week closed index for the experimental data, experimental results show the effectiveness of this approach.
DOI:10.1109/CIS.2014.22