用双向层次网络提升通胀预测精度，更好捕捉商品分类间信息流动。
BiHRNN -- Bi-Directional Hierarchical Recurrent Neural Network for Inflation Forecasting
- 构建双向层次RNN，实现不同层级物价数据的信息双向传递。
- 在美加挪三国数据上，预测误差显著低于传统RNN模型。
- 适合关注宏观经济预测与时间序列建模的研究者使用。
通胀预测对利率决策、投资和工资制定至关重要，但受动态因素及消费者价格指数（CPI）多层结构影响，准确预测困难。本文提出双向层次循环神经网络（BiHRNN），利用层级结构实现上下层间双向信息流动，并通过参数约束提升各层级预测精度，避免统一模型的效率损失。我们在美国、加拿大和挪威的通胀数据集上进行训练、超参数调优及多种损失函数实验。结果表明，BiHRNN显著优于传统RNN模型，其双向架构在提升预测准确性方面起关键作用。
原文摘要 · Abstract (English)
Inflation prediction guides decisions on interest rates, investments, and wages, playing a key role in economic stability. Yet accurate forecasting is challenging due to dynamic factors and the layered structure of the Consumer Price Index, which organizes goods and services into multiple categories. We propose the Bi-directional Hierarchical Recurrent Neural Network (BiHRNN) model to address these challenges by leveraging the hierarchical structure to enable bidirectional information flow between levels. Informative constraints on the RNN parameters enhance predictive accuracy at all levels without the inefficiencies of a unified model. We validated BiHRNN on inflation datasets from the United States, Canada, and Norway by training, tuning hyperparameters, and experimenting with various loss functions. Our results demonstrate that BiHRNN significantly outperforms traditional RNN models, with its bidirectional architecture playing a pivotal role in achieving improved forecasting accuracy.
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