标题： 通过迁移学习改进时间序列估计和预测 显示英文标题

标题： Improving time series estimation and prediction via transfer learning

摘要： 在文献中有很多高维但样本量有限的时间序列，例如宏观经济变量，仅使用相应的数据集几乎不可能获得有效的估计和准确的预测。 本文通过引入一种基于表示的迁移学习框架来填补这一空白，用于向量自回归模型，可以从相关源数据集中丰富的观测信息中提取，通过表示学习提高估计效率。 提出了一种两阶段的正则化估计过程，具有良好的非渐近性质，并建议使用交替更新算法来寻找估计值。 我们的迁移学习框架可以处理样本量不同的时间序列以及异步开始和/或结束时间点，从而在整合来自不同数据集的信息方面提供了显著的灵活性。 进行了模拟实验以评估所提出方法的小样本性能，并通过日本和其他九个国家的20个宏观经济变量的经验分析证明了其有效性。 显示英文摘要

摘要： 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.