Improving time series estimation and prediction via transfer learning

TL;DR

This paper introduces a transfer learning framework for high-dimensional time series, improving estimation and prediction by leveraging related datasets through a novel representation-based approach and regularized estimation.

Contribution

It proposes a new transfer learning method for vector autoregressive models that handles diverse datasets with varying sample sizes and asynchronous time points.

Findings

Enhanced estimation efficiency demonstrated in simulations

Framework effectively integrates diverse macroeconomic datasets

Empirical analysis shows improved prediction accuracy

Abstract

There are many time series in the literature with high dimension yet limited sample sizes, such as macroeconomic variables, and it is almost impossible to obtain efficient estimation and accurate prediction by using the corresponding datasets themselves. This paper fills the gap by introducing a novel representation-based transfer learning framework for vector autoregressive models, and information from related source datasets with rich observations can be leveraged to enhance estimation efficiency through representation learning. A two-stage regularized estimation procedure is proposed with well established non-asymptotic properties, and algorithms with alternating updates are suggested to search for the estimates. Our transfer learning framework can handle time series with varying sample sizes and asynchronous starting and/or ending time points, thereby offering remarkable flexibility in integrating information from diverse datasets. Simulation experiments are conducted to evaluate the finite-sample performance of the proposed methodology, and its usefulness is demonstrated by an empirical analysis on 20 macroeconomic variables from Japan and another nine countries.