A multi-task encoder-dual-decoder framework for mixed frequency data prediction

Abstract

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.