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

Olcay Ozupek, Reyat Yilmaz, Bita Ghasemkhani (), Derya Birant and Recep Alp Kut
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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

Mathematics, 2024, vol. 12, issue 17, 1-36

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.

Keywords: mathematics; machine learning; financial forecasting; price prediction; long short-term memory; deep learning; time series; empirical mode decomposition; technical indicators; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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