基于BSFinformer模型的金融数据特征选择及预测OA北大核心CSTPCD

The long-term series prediction in the financial domain faces challenges due to complex markets and numerous financial products.Traditional methods in time series forecasting perform well in handling linear distributed data,but their effectiveness is limited when dealing with redundant feature parameters and nonlinear data of long sequence financial products.To address this issue,this study proposes a method in long-term series prediction called BSFinformer(Boruta-SHAP+Finformer).This method leverages the time correlation of financial data and integrates techniques such as Boruta-SHAP and Finformer to accomplish feature selection and prediction tasks.Firstly,the Boruta-SHAP module is introduced,which utilizes such analytical methods as XgBoost and SHAP for feature selection.It identifies important features related to tasks of financial time series prediction from the given feature set and explains the impact of these features on the prediction.Secondly,the Finformer module is developed by improving the Transformer structure and incorporating self-attention mechanisms.It decomposes long sequence financial data into trend,cycle,and residual components,and combines sparse self-attention mechanisms.The BSFinformer model is evaluated on multiple real financial datasets through experiments.The experimental results demonstrate that the BSFinformer model exhibits excellent performance in price prediction of financial products.Compared to other forecasting methods,the BSFinformer model accurately captures long-term trends and periodicity to achieve high-quality predictions.Specifically,compared to the traditional Transformer model,the BSFinformer model reduces Mean-Square Error by 52％,16％and reduces 19％,and Mean Absolute Error by 34％,25％and 11％on the three datasets,respectively.It provides an effective solution for long-term series prediction of financial data.