中南民族大学学报（自然科学版）2026，Vol.45Issue(3)：392-400,9.DOI:10.20056/j.cnki.ZNMDZK.20250849
基于多任务学习的金融时间序列预测研究
Research on financial time series prediction based on multi-task learning
摘要
Abstract
In the domain of quantitative investment,achieving maximized returns and minimized risks during continuous trading periods is a pivotal task.Accurate forecasting of financial time series plays a crucial role in this process.The concept of multi-task learning is introduced innovatively and an information feedback mechanism is incorporated,significantly enhancing the model's capabilities.The advancement allows the model to not only perform multi-dimensional predictions but also simultaneously increase quantitative trading profits while reducing investment risks.The research initially utilizes actual stock data to predict the price changes a week ahead,incorporating these predictions as a critical factor in the stock backtesting scoring system.The method can improve the accuracy of predicting stock trends to enhance investment returns and effectively reduce risks.Further,by replacing historical data with model-predicted future price changes for a week ahead,the stock scoring system is optimized,thereby improving the accuracy of stock trend predictions.Experimental results demonstrate that compared to predictions based solely on daily price changes,the application of Transformer,LSTM,and IGMTF models significantly enhances prediction accuracy and achieves a noticeable increase in returns per unit of risk.关键词
时间序列预测/机器学习/多任务学习/股票预测/特征融合Key words
time series prediction/machine learning/multi-task learning/stock forecast/feature fusion分类
信息技术与安全科学引用本文复制引用
胡志博,叶正,葛君..基于多任务学习的金融时间序列预测研究[J].中南民族大学学报（自然科学版）,2026,45(3):392-400,9.基金项目
教育部产学合作协同育人资助项目(202102191002) （202102191002）
中南民族大学引进人才资助项目(YZZ20001) （YZZ20001）
中央高校科研业务费专项资金资助项目(CZZ24009) （CZZ24009）
