A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM
作者：
Highlights：
• A novel FTS forecasting methodology based on deep learning is proposed.
• Proposed model exhibits highest predictive accuracy and directional symmetry.
• Deep learning hybrid strategy yields stable excess returns and avoids drawdown risk.
• Test error indicators generally drop as the stock markets maturity degree increases.
• Methodology goes beyond a pure financial market application.
摘要
•A novel FTS forecasting methodology based on deep learning is proposed.•Proposed model exhibits highest predictive accuracy and directional symmetry.•Deep learning hybrid strategy yields stable excess returns and avoids drawdown risk.•Test error indicators generally drop as the stock markets maturity degree increases.•Methodology goes beyond a pure financial market application.
论文关键词：Deep learning,Long short-term memory,Complementary ensemble empirical mode decomposition,Financial time series,Stock market forecasting,Principal component analysis
论文评审过程：Received 17 October 2019, Revised 15 April 2020, Accepted 26 May 2020, Available online 30 May 2020, Version of Record 17 June 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113609
