Forecasting PPI components using a hybrid hierarchical prediction framework with parameter adaptive transfer algorithm

The accuracy of economic forecasting directly influences the formulation of economic policies and profoundly impacts the stable operation of the economy. As a pivotal indicator of economic activity, predicting the producer price index (PPI) is crucial. Although most existing research is focused on the overall PPI, economic and financial institutions are increasingly interested in its partially disaggregated components. Therefore, this paper proposes a hybrid hierarchical deep network prediction framework called Attention-HRNN-GRU (AHG) with parameter adaptive transfer, which integrates algorithms such as the attention mechanism, the Hierarchical Recurrent Neural Network (HRNN) and the Gated Recurrent Unit (GRU). First, an independent GRU network is designed and trained separately for each PPI level to perform preliminary predictions. The internal parameters of each level’s network are preserved to facilitate interlevel information transfer, forming an initial HRNN framework. An attention mechanism is then introduced to adaptively adjust the parameters of the upper-level prediction model that are used as the prediction parameters for the lower-level model. This process enables effective information transfer across multiple levels, producing high-accuracy prediction outcomes. This method effectively addresses a common issue in traditional hierarchical data prediction, where the direct application of upper-level parameters to lower-level data often overlooks variations between sequences. Experimental results show that the proposed AHG model markedly reduces prediction errors compared with those of the current advanced HRNN model. For example, the root mean square error (RMSE) of the producing materials index improved by 13.11% over that of the advanced model.