Adaptively weighted support vector regression for financial time series prediction

Author

Zhijie Li ; Yuanxiang Li ; Fei Yu ; Dahai Ge

Author_Institution

State Key Lab. of Software Eng., Wuhan Univ., Wuhan, China

fYear

2014

fDate

6-11 July 2014

Firstpage

3062

Lastpage

3065

Abstract

The financial data are usually volatile and contain outliers. One problem of the standard support vector regression (SVR) for financial time series prediction is that it considers data in a fixed fashion only and lack the robustness to outliers. To tackle this issue, we propose the adaptively weighted support vector regression (AWSVR) model. This novel model is demonstrated to choose the weights adaptively with data. Therefore, the AWSVR can tolerate noise adaptively. The experimental results on three indices: the NASDAQ, the Standard & Poor 500 index (S&P), and the FSTE100 index (FSTE) show its advantages over the standard SVR.

Keywords

finance; prediction theory; regression analysis; support vector machines; time series; FSTE100 index; NASDAQ; Standard and Poor 500 index; adaptively weighted support vector regression model; financial data; financial time series prediction; Approximation methods; Indexes; Noise; Robustness; Standards; Support vector machines; Time series analysis; Support vector regression; data adaptive learning; financial time series prediction; outliers; weighted learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), 2014 International Joint Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6627-1

Type

conf

DOI

10.1109/IJCNN.2014.6889426

Filename

6889426