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

被引:1
作者
Ozupek, Olcay [1 ]
Yilmaz, Reyat [2 ]
Ghasemkhani, Bita [1 ]
Birant, Derya [3 ]
Kut, Recep Alp [3 ]
机构
[1] Dokuz Eylul Univ, Grad Sch Nat & Appl Sci, TR-35390 Izmir, Turkiye
[2] Dokuz Eylul Univ, Dept Elect & Elect Engn, TR-35390 Izmir, Turkiye
[3] Dokuz Eylul Univ, Dept Comp Engn, TR-35390 Izmir, Turkiye
关键词
mathematics; machine learning; financial forecasting; price prediction; long short-term memory; deep learning; time series; empirical mode decomposition; technical indicators; artificial intelligence;
D O I
10.3390/math12172794
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
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页数:36
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