基于双模型并联的复杂时序预测方法OA北大核心

Traditional forecasting models typically focus on capturing trends and patterns in time series data while often neglecting interactions among variables,which limits their effectiveness in complex time series scenarios.To address this,a dual-model parallel approach named Dualformer is proposed,which integrates the inverted transformer(iTransformer)and patch time series transformer(PatchTST)models in a parallel architecture.In this framework,the conventional feedforward neural network components are replaced with activation functions,and a multilayer perceptron is introduced to further process the outputs of the two parallel sub-models.The Dualformer model employs attention mechanisms to simultaneously capture both the temporal and variable dimensions in the complex time series,effectively capturing time-dependent patterns and multivariate interactions.Experimental results demonstrate that the Dualformer significantly outperforms the benchmark models,including iTransformer,PatchTST,and decomposition linear(DLinear)in complex time series forecasting tasks.These results highlight Dualformer's superior accuracy and its strong potential for real-world applications in forecasting complex time series data.