论文标题
具有分层复发神经网络的预测CPI通胀组件
Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks
论文作者
论文摘要
我们提出了基于复发性神经网络（RNN）的层次结构，用于预测消费者价格指数（CPI）的分类通货膨胀组件。尽管大多数现有研究主要集中在预测通货膨胀标题上，但许多经济和金融实体对其部分分类组件更感兴趣。为此，我们开发了新型的分层复发性神经网络（HRNN）模型，该模型利用CPI层次结构中较高级别的信息来改善较低较低级别的预测。我们的评估基于美国CPI-U指数的大型数据集，表明HRNN模型的表现明显优于大量众所周知的通货膨胀预测基线。
We present a hierarchical architecture based on Recurrent Neural Networks (RNNs) for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused mainly on predicting the inflation headline, many economic and financial entities are more interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model that utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Our evaluations, based on a large data-set from the US CPI-U index, indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines.
