Mixed-frequency data prediction tasks are pertinent in various application domains, in which one leverages progressively available high-frequency data to forecast/nowcast the low-frequency ones. Existing methods in the literature tailored to such tasks are mostly linear in nature; depending on the specific formulation, they largely rely on the assumption that the (latent) processes that govern the dynamics of the high- and low-frequency blocks of variables evolve at the same frequency, either the low or the high one. This paper develops a neural network-based multi-task shared-encoder-dual-decoder framework for joint multi-horizon prediction of both the low- and high-frequency blocks of variables, wherein the encoder/decoder modules can be either long short-term memory or transformer ones. It addresses forecast/nowcast tasks in a unified manner, leveraging the encoder–decoder structure that can naturally accommodate the mixed-frequency nature of the data. The proposed framework exhibited competitive performance when assessed on both synthetic data experiments and two real datasets of US macroeconomic indicators and electricity data.

混合频率数据预测任务与各种应用领域相关，其中利用逐渐可用的高频数据来预测/即时预报低频数据。文献中针对此类任务量身定制的现有方法本质上大多是线性的；根据具体的公式，它们很大程度上依赖于这样的假设：控制高频和低频变量块动态的（潜在）过程以相同的频率（低频率或高频率）演化。本文开发了一种基于神经网络的多任务共享编码器双解码器框架，用于低频和高频变量块的联合多水平预测，其中编码器/解码器模块可以是长的、短的、术语记忆或变压器。它以统一的方式处理预报/临近预报任务，利用可以自然地适应数据的混合频率性质的编码器-解码器结构。在对合成数据实验以及美国宏观经济指标和电力数据的两个真实数据集进行评估时，所提出的框架表现出了竞争性的性能。