通过深度可分离神经网络进行混合频率时间序列预测

中文摘要 AI 辅助

为更好地预测混合频率时间序列，关键在于选择合适的频率对齐方式。现有方法局限于线性变换，可能忽略非线性，导致预测不佳。本文考虑为每个频率对齐使用深度神经网络，即深度可分离神经网络。各阶段对齐采用参数共享机制，可构建更深网络处理高频预测变量。本文建立了该网络的近似理论并推导了非渐近预测误差界。模拟研究证明了方法的有限样本性能，实证应用表明其预测精度优于现有混合频率方法。

英文摘要

To better forecast mixed-frequency time series, it is the key to choose a suitable way for frequency alignment. However, the existing methods are all limited to linear transformations, and this may overlook the possible nonlinearity, leading to a worse prediction. We alternatively consider a deep neural network for each frequency alignment, and hence a depth-separable neural network. Moreover, a parameter-sharing mechanism is adopted across the alignment at each stage, making possible a deeper network for a large set of higher-frequency predictors. This paper establishes an approximation theory for the proposed depth-separable network, and a non-asymptotic prediction error bound is also derived. Simulation studies demonstrate the finite-sample performance of the proposed method, and an empirical application to forecasting U.S. quarterly macroeconomic variables using monthly and daily indicators, highlights its superior predictive accuracy over existing mixed-frequency methods.

发表机构

• The University of Hong Kong（香港大学）
• Shanghai University of Finance and Economics（上海财经大学）

机构由 AI 辅助整理，请以论文原文为准。