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A Novel Hybrid Model for Financial Forecasting Based on CEEMDAN-SE and ARIMA-CNN-LSTM

Author

Listed:
  • Zefan Dong

    (School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550003, China)

  • Yonghui Zhou

    (School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550003, China)

Abstract

Financial time series data are characterized by non-linearity, non-stationarity, and stochastic complexity, so predicting such data presents a significant challenge. This paper proposes a novel hybrid model for financial forecasting based on CEEMDAN-SE and ARIMA- CNN-LSTM. With the help of the CEEMDAN-SE method, the original data are decomposed into several IMFs and reconstructed via sample entropy into a lower-complexity stationary high-frequency component and a low-frequency component. The high-frequency component is predicted by the ARIMA statistical forecasting model, while the low-frequency component is predicted by a neural network model combining CNN and LSTM. Compared to some classical prediction models, our algorithm exhibits superior performance in terms of three evaluation indexes, namely, RMSE, MAE, and MAPE, effectively enhancing model accuracy while reducing computational overhead.

Suggested Citation

  • Zefan Dong & Yonghui Zhou, 2024. "A Novel Hybrid Model for Financial Forecasting Based on CEEMDAN-SE and ARIMA-CNN-LSTM," Mathematics, MDPI, vol. 12(16), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2434-:d:1450541
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    References listed on IDEAS

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    1. Ma, Yao & Yang, Baochen & Su, Yunpeng, 2020. "Technical trading index, return predictability and idiosyncratic volatility," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 879-900.
    2. E, Jianwei & Ye, Jimin & Jin, Haihong, 2019. "A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    3. Deepak Gupta & Mahardhika Pratama & Zhenyuan Ma & Jun Li & Mukesh Prasad, 2019. "Financial time series forecasting using twin support vector regression," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.
    4. Mojtaba Nabipour & Pooyan Nayyeri & Hamed Jabani & Amir Mosavi, 2020. "Deep learning for Stock Market Prediction," Papers 2004.01497, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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