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

Jiahe Lin and George Michailidis

International Journal of Forecasting, 2024, vol. 40, issue 3, 942-957

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.

Keywords: Mixed-frequency data; Encoder–decoder; LSTM; Transformer; Multi-horizon forecasts; Nowcasting (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:3:p:942-957

DOI: 10.1016/j.ijforecast.2023.08.003

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