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Fusion of multiple indicators with ensemble incremental learning techniques for stock price forecasting

  • Xueheng Qiu
  • Ponnuthurai Nagaratnam SuganthanEmail author
  • Gehan A. J. Amaratunga
Original Article
  • 8 Downloads

Abstract

Predicting stock market index is very challenging as financial time series shows highly non-linear and non-stationary patterns. In this paper, an ensemble incremental learning model is presented for stock price forecasting, which is composed of two decomposition methods: discrete wavelet transform (DWT) and empirical mode decomposition (EMD), as well as two learning models: random vector functional link network (RVFL) and support vector regression (SVR). Firstly, DWT and EMD are sequentially combined to decompose the historical stock price time series, followed by RVFL models to analyze the obtained sub-signals and generate predictions. Moreover, ten stock market indicators are used to improve the performance of the ensemble model. Last but not least, incremental learning with RVFL also benefits the performance significantly. To evaluate the proposed DWT-EMD-RVFL-SVR model, stock price forecasting for five power related companies are conducted to compare with seven benchmark methods and two recently published works.

Keywords

Discrete wavelet transform Empirical mode decomposition Predictors fusion Incremental learning Stock price forecasting Support vector regression Artificial neural network Random vector functional link network 

Notes

Acknowledgements

This project is funded by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.

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Copyright information

© Institute for Development and Research in Banking Technology 2018

Authors and Affiliations

  • Xueheng Qiu
    • 1
  • Ponnuthurai Nagaratnam Suganthan
    • 1
    Email author
  • Gehan A. J. Amaratunga
    • 2
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Centre for Advanced Photonics and Electronics, Electrical Engineering Division, Engineering DepartmentUniversity of CambridgeCambridgeUK

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