Financial Time Series Forecasting: A Comparison Between Traditional Methods and AI-Driven Techniques
DOI:
https://doi.org/10.71222/339b9812Keywords:
financial time series forecasting, machine learning, arima, AI -driven techniques, machine learning, deep learningAbstract
Financial time series forecasting plays a crucial role in predicting future market trends, pricing assets, and managing risks in financial markets. This paper compares traditional methods, such as ARIMA, Exponential Smoothing, and GARCH, with AI-driven techniques, including machine learning and deep learning models, for financial time series forecasting. Traditional models are well-established and effective for stationary data, but they struggle with non-linear relationships and large datasets. In contrast, AI-driven techniques, such as Random Forests, Long Short-Term Memory Networks (LSTMs), and reinforcement learning, offer improved accuracy and adaptability by capturing complex patterns in the data. However, these models come with higher computational complexity and challenges related to interpretability. The paper provides a comprehensive comparison of these methods, highlighting their strengths, limitations, and practical applications. It concludes by offering recommendations for when to use traditional methods versus AI-driven approaches, based on the nature of the data and forecasting needs. The integration of AI with traditional models is also discussed as a promising future direction in financial forecasting.
References
1. Y. Zhao, W. Zhang, and X. Liu, "Grid search with a weighted error function: Hyper-parameter optimization for financial time series forecasting," Appl. Soft Comput., vol. 154, p. 111362, 2024, doi: 10.1016/j.asoc.2024.111362.
2. C. Zhang, N. N. A. Sjarif, and R. Ibrahim, "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022," Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 14, no. 1, p. e1519, 2024, doi: 10.1002/widm.1519.
3. T. Blasco, J. S. Sánchez, and V. García, "A survey on uncertainty quantification in deep learning for financial time series prediction," Neurocomputing, vol. 576, p. 127339, 2024, doi: 10.1016/j.neucom.2024.127339.
4. S. A. Edalatpanah, F. S. Hassani, F. Smarandache, A. Sorourkhah, D. Pamucar, and B. Cui, "A hybrid time series forecasting method based on neutrosophic logic with applications in financial issues," Eng. Appl. Artif. Intell., vol. 129, p. 107531, 2024, doi: 10.1016/j.engappai.2023.107531.
5. S. S. W. Fatima and A. Rahimi, "A review of time-series forecasting algorithms for industrial manufacturing systems," Ma-chines, vol. 12, no. 6, p. 380, 2024, doi: 10.3390/machines12060380.
6. M. Mohammadi, S. Jamshidi, A. Rezvanian, M. Gheisari, and A. Kumar, "Advanced fusion of MTM-LSTM and MLP models for time series forecasting: An application for forecasting the solar radiation," Meas. Sens., vol. 33, p. 101179, 2024, doi: 10.1016/j.measen.2024.101179.
7. R. P. Varshney and D. K. Sharma, "Optimizing time-series forecasting using stacked deep learning framework with enhanced adaptive moment estimation and error correction," Expert Syst. Appl., vol. 2024, p. 123487, 2024, doi: 10.1016/j.eswa.2024.123487.
8. J. Cheng, S. Tiwari, D. Khaled, M. Mahendru, and U. Shahzad, "Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models," Technol. Forecast. Soc. Change, vol. 198, p. 122938, 2024, doi: 10.1016/j.techfore.2023.122938.
9. D. Yao and K. Yan, "Time series forecasting of stock market indices based on DLWR-LSTM model," Finance Res. Lett., vol. 68, p. 105821, 2024, doi: 10.1016/j.frl.2024.105821.
10. W. Li and K. E. Law, "Deep learning models for time series forecasting: A review," IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3422528.
11. A. Bhambu, R. Gao, and P. N. Suganthan, "Recurrent ensemble random vector functional link neural network for financial time series forecasting," Appl. Soft Comput., vol. 161, p. 111759, 2024, doi: 10.1016/j.asoc.2024.111759.
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Copyright (c) 2025 Gwokkwan Sun, Shuhan Deng (Author)
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