基于APDFinformer模型的金融数据的多元时序预测OA北大核心CSTPCD

Recently,the prediction of Multivariate Time Series(MTS)has gradually come into focus,especially with many Transformer-based models showing tremendous potential.However,existing Transformer-based models mainly focus on modeling cross-temporal dependencies,often overlooking the dependencies among different variables,which are crucial for MTS prediction.Therefore,this paper proposes a novel Multivariate Time Series prediction model,namely APDFinformer,designed to address the complex and dynamic nature of financial markets.The model integrates the Adaptive Multi-Scale Identifier(AMSI),which extracts information from time series at different scales,helping reduce the impact of noise on time series and capture the interactions across different scales.Additionally,for the processed Multivariate Time Series data,the model utilizes the Decomposition method to divide it into trend and seasonal components.The trend component undergoes a simple linear processing,while the seasonal component,following the PatchTST approach,is sliced to shorten the sequence length,representing local features.This is advantageous for retaining local semantic information,facilitating the model's analysis of the correlations between time steps.Experimental results demonstrate that compared to traditional methods and various models similar to the Transformer model,APDFinformer more accurately captures the complex dynamics of financial markets and exhibits higher prediction accuracy.Specifically,compared to the Transformer model,APDFinformer reduces the MSE(Mean Squared Error)by 54％,24％,and 60％on the three selected cryptocurrency datasets,along with a reduction in MAE(Mean Absolute Error)by 39％,22％,and 44％.This study suggests that APDFinformer is a more reliable prediction tool for MTS in the financial domain and provides valuable insights for other application domains based on the Transformer model,meeting the evolving demands of financial markets.