Research on Stock Prediction Model using Mode Decomposition Algorithm and Support Vector Regression

• Published in: ACM International Conference Proceeding Series
• Main Author: Sun Y.; Mutalib S.; Omar; Peng
• Format: Conference paper
• Language: English
• Published: Association for Computing Machinery 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200403842&doi=10.1145%2f3675417.3675480&partnerID=40&md5=4edb2c31f33faf4ab7dc9f62b4932259

Stock market forecasting has always been one of the most difficult challenges faced by the stock market. In the issue of stock price prediction, its noisy nature, complexity, as well as the country's governance system, economic situation, epidemic will be mentioned. In recent years, scholars have conducted many stock market prediction studies using traditional and artificial intelligence technologies since the lower the error in stock prediction, the more investment risk can be reduced. In this research, a novel prediction method based on mode decomposition algorithm and optimized Support Vector Regression (SVR) is proposed. Mode decomposition algorithm is used to smooth the original signal and reduce the impact of noise. SVR extracts knowledge from data and ultimately utilizes the knowledge to predict new stock data. Bayesian optimization (BO) algorithm is adopted to optimize the core parameters of SVR and prevent overfitting so as to improve predictive performance. Finally, The CEEMDAN-BO-SVR model proposed in this study is compared with several other methods, including CEEMDAN-SVR, BO-SVR, SVR, RNN, LSTM, and ensemble learning algorithms, then analyzed through evaluation indicators. The experimental results comparing different models indicate that the CEEMDAN-BO-SVR model has the best fitting ability. © 2024 ACM.