An AI-Based Learning Algorithm Study for Time-Sequencing Model Forecasting and Prediction

• Published in: 2024 7th International Conference on Mechatronics and Computer Technology Engineering (MCTE) pp. 1233 - 1237
• Main Authors: Ling, Mingcheng, Qi, Weimin, Zhang, Xia, Wang, Meng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.08.2024
• Subjects: Artificial Intelligence; Data models; Forecasting; Investment; Machine learning; Machine learning algorithms; Mathematical models; Optimization; Prediction algorithms; Predictive models; Social networking (online); Time-Sequencing Forecasting and Prediction
• DOI: 10.1109/MCTE62870.2024.11118034

Machine learning has been applied to time-sequencing model prediction with the aim of making more informed decisions at a certain time. In particular, time-sequencing model forecasting such as stock market prediction using machine learning involves the analysis of various factors, including historical curve of price data, financial statements, news, and social media data. Machine learning algorithms can be trained to identify patterns and relationships among these factors and make predictions about future curve and trend. The proposed AI-based learning algorithm has proved its potential to improve the accuracy of the predictions and help investors make more informed investment decisions. A buy-or-sell trading strategy is generated according to our model's predictions. The proposed model can help investors to make an ideal choice while selling, holding, or buying shares. It is important to note that economical trading market prediction is not an exact mathematically scientific problem, and even the most advanced machine learning algorithms may not be able to accurately predict with complete certainty.