Version 1
: Received: 10 July 2024 / Approved: 10 July 2024 / Online: 11 July 2024 (12:24:28 CEST)
How to cite:
Zheng, H.; Wu, J.; Song, R.; Guo, L.; Xu, Z. Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis. Preprints2024, 2024070895. https://doi.org/10.20944/preprints202407.0895.v1
Zheng, H.; Wu, J.; Song, R.; Guo, L.; Xu, Z. Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis. Preprints 2024, 2024070895. https://doi.org/10.20944/preprints202407.0895.v1
Zheng, H.; Wu, J.; Song, R.; Guo, L.; Xu, Z. Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis. Preprints2024, 2024070895. https://doi.org/10.20944/preprints202407.0895.v1
APA Style
Zheng, H., Wu, J., Song, R., Guo, L., & Xu, Z. (2024). Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis. Preprints. https://doi.org/10.20944/preprints202407.0895.v1
Chicago/Turabian Style
Zheng, H., Lingfeng Guo and Zeqiu Xu. 2024 "Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis" Preprints. https://doi.org/10.20944/preprints202407.0895.v1
Abstract
This paper explores the application of machine learning in financial time series analysis, focusing on predicting trends in financial enterprise stocks and economic data. It begins by distinguishing stocks from stocks and elucidates risk management strategies in the stock market. Traditional statistical methods such as ARIMA and exponential smoothing are discussed in terms of their advantages and limitations in economic forecasting. Subsequently, the effectiveness of machine learning techniques, particularly LSTM and CNN-BiLSTM hybrid models, in financial market prediction is detailed, highlighting their capability to capture nonlinear patterns in dynamic markets. The study demonstrates the advancements in predictive accuracy and robustness achieved by deep learning methods through empirical analysis and model validation. The findings contribute significantly to academic discourse and offer practical insights for investors, financial analysts, and policymakers navigating market volatility and optimizing investment strategies. Finally, the paper outlines prospects for machine learning in financial forecasting, laying a theoretical foundation and methodological framework for achieving more precise and reliable economic predictions.
Keywords
Machine learning; Financial time series analysis; LSTM; CNN-BiLSTM hybrid models; Stock market prediction
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
