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Transfer Learning for Financial Time Series Forecasting

  • Qi-Qiao He
  • Patrick Cheong-Iao Pang
  • Yain-Whar SiEmail author
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11671)

Abstract

Time-series are widely used for representing non-stationary data such as weather information, health related data, economic and stock market indexes. Many statistical methods and traditional machine learning techniques are commonly used for forecasting time series. With the development of deep learning in artificial intelligence, many researchers have adopted new models from artificial neural networks for forecasting time series. However, poor performance of applying deep learning models in short time series hinders the accuracy in time series forecasting. In this paper, we propose a novel approach to alleviate this problem based on transfer learning. Existing work on transfer learning uses extracted features from a source dataset for prediction task in a target dataset. In this paper, we propose a new training strategy for time-series transfer learning with two source datasets that outperform existing approaches. The effectiveness of our approach is evaluated on financial time series extracted from stock markets. Experiment results show that transfer learning based on 2 data sets is superior than other base-line methods.

Keywords

Transfer learning Financial time series Forecasting Artificial neural networks 
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Notes

Acknowledgement

The research was funded by the Research Committee of University of Macau, Grant MYRG2018-00246-FST.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Qi-Qiao He
    • 1
  • Patrick Cheong-Iao Pang
    • 2
  • Yain-Whar Si
    • 1
    Email author
  1. 1.Department of Computer and Information ScienceUniversity of MacauTaipaMacau
  2. 2.School of Computing and Information SystemsThe University of MelbourneParkvilleAustralia

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