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Predicting financial enterprise stocks and economic data trends using machine learning time series analysis
- 1 Electrical & Computer Engineering, New York University, New York, NY, USA
- 2 Computer Science, University of Southern California, Los Angeles, CA, USA
- 3 Information System & Technology Data Analytics, California State University, CA, USA
- 4 Business Analytics, Trine University, AZ, USA
- 5 Information Networking, Carnegie Mellon University, PA, USA
* Author to whom correspondence should be addressed.
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. 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
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Cite this article
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. Applied and Computational Engineering,87,26-32.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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