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A Novel Hybrid Model (EMD-TI-LSTM) for Enhanced Financial Forecasting with Machine Learning

Author

Listed:
  • Olcay Ozupek

    (Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, Turkey)

  • Reyat Yilmaz

    (Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir 35390, Turkey)

  • Bita Ghasemkhani

    (Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, Turkey)

  • Derya Birant

    (Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey)

  • Recep Alp Kut

    (Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey)

Abstract

Financial forecasting involves predicting the future financial states and performance of companies and investors. Recent technological advancements have demonstrated that machine learning-based models can outperform traditional financial forecasting techniques. In particular, hybrid approaches that integrate diverse methods to leverage their strengths have yielded superior results in financial prediction. This study introduces a novel hybrid model, entitled EMD-TI-LSTM, consisting of empirical mode decomposition (EMD), technical indicators (TI), and long short-term memory (LSTM). The proposed model delivered more accurate predictions than those generated by the conventional LSTM approach on the same well-known financial datasets, achieving average enhancements of 39.56%, 36.86%, and 39.90% based on the MAPE, RMSE, and MAE metrics, respectively. Furthermore, the results show that the proposed model has a lower average MAPE rate of 42.91% compared to its state-of-the-art counterparts. These findings highlight the potential of hybrid models and mathematical innovations to advance the field of financial forecasting.

Suggested Citation

  • Olcay Ozupek & Reyat Yilmaz & Bita Ghasemkhani & Derya Birant & Recep Alp Kut, 2024. "A Novel Hybrid Model (EMD-TI-LSTM) for Enhanced Financial Forecasting with Machine Learning," Mathematics, MDPI, vol. 12(17), pages 1-36, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2794-:d:1474639
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    References listed on IDEAS

    as
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    3. Junwei Chen, 2023. "Analysis of Bitcoin Price Prediction Using Machine Learning," JRFM, MDPI, vol. 16(1), pages 1-25, January.
    4. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
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