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AdaBoost-LSTM Ensemble Learning for Financial Time Series Forecasting

  • Shaolong Sun
  • Yunjie WeiEmail author
  • Shouyang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10862)

Abstract

A hybrid ensemble learning approach is proposed to forecast financial time series combining AdaBoost algorithm and Long Short-Term Memory (LSTM) network. Firstly, by using AdaBoost algorithm the database is trained to get the training samples. Secondly, the LSTM is utilized to forecast each training sample separately. Thirdly, AdaBoost algorithm is used to integrate the forecasting results of all the LSTM predictors to generate the ensemble results. Two major daily exchange rate datasets and two stock market index datasets are selected for model evaluation and comparison. The empirical results demonstrate that the proposed AdaBoost-LSTM ensemble learning approach outperforms some other single forecasting models and ensemble learning approaches. This suggests that the AdaBoost-LSTM ensemble learning approach is a highly promising approach for financial time series data forecasting, especially for the time series data with nonlinearity and irregularity, such as exchange rates and stock indexes.

Keywords

Financial time series forecasting Long short-term memory network AdaBoost algorithm Ensemble learning 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  2. 2.School of Economics and ManagementUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.Center for Forecasting ScienceChinese Academy of SciencesBeijingChina

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