CUED Publications database

Stock price forecasting with empirical mode decomposition based ensemble ν-Support Vector regression model

Qiu, X and Zhu, H and Suganthan, PN and Amaratunga, GAJ (2017) Stock price forecasting with empirical mode decomposition based ensemble ν-Support Vector regression model. In: UNSPECIFIED pp. 22-34..

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Abstract

Stock price forecasting is one of the most challenging tasks of time series forecasting due to the inherent non-linearity and non-stationary characteristics of the stock market and financial time series. In this paper, an ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and ν-Support Vector Regression (ν-SVR) is presented for short-term stock price forecasting. First of all, the historical stock price time series were decomposed into several intrinsic mode functions (IMFs). Then each IMF was modeled by a ν-SVR model to generate the corresponding forecasting IMF value. Finally, the prediction results of all IMFs were combined to formulate an aggregated output for stock price. The stock market price datasets of three power related companies are used to test the effectiveness of the proposed EMD-ν-SVR method. Simulation results demonstrated attractiveness of the proposed method compared with six forecasting methods.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div B > Electronics, Power & Energy Conversion
Depositing User: Cron Job
Date Deposited: 13 Nov 2017 20:08
Last Modified: 10 Apr 2021 22:28
DOI: 10.1007/978-981-10-6427-2_2