标题： 在新冠疫情危机中长短期记忆人工神经网络在实时预测中的表现 显示英文标题

标题： Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis

摘要： 新冠疫情凸显了政策制定者对及时估计宏观经济变量的日益增长的需求。 一份先前的联合国贸发会议研究报告考察了长短期记忆人工神经网络（LSTM）在进行此类经济实时预测中的适用性。 在此，LSTM在新冠疫情期间的表现与动态因子模型（DFM）进行了比较和对比，DFM是该领域常用的分析方法。 三个独立变量，即全球商品出口价值和数量以及全球服务出口，使用实际数据版本进行实时预测，并对2020年第二、第三和第四季度以及2021年第一和第二季度的表现进行了评估。 无论是平均绝对误差还是均方根误差，LSTM在三分之二的变量/季度组合中表现更好，并且展示了更渐进的预测演变，具有更一致的叙述和更小的修正。 此外，一种用于为LSTMs引入可解释性的方法被介绍并提供在配套的nowcast_lstm Python库中，该库现在也提供在R、MATLAB和Julia中。 显示英文摘要

摘要： The COVID-19 pandemic has demonstrated the increasing need of policymakers for timely estimates of macroeconomic variables. A prior UNCTAD research paper examined the suitability of long short-term memory artificial neural networks (LSTM) for performing economic nowcasting of this nature. Here, the LSTM's performance during the COVID-19 pandemic is compared and contrasted with that of the dynamic factor model (DFM), a commonly used methodology in the field. Three separate variables, global merchandise export values and volumes and global services exports, were nowcast with actual data vintages and performance evaluated for the second, third, and fourth quarters of 2020 and the first and second quarters of 2021. In terms of both mean absolute error and root mean square error, the LSTM obtained better performance in two-thirds of variable/quarter combinations, as well as displayed more gradual forecast evolutions with more consistent narratives and smaller revisions. Additionally, a methodology to introduce interpretability to LSTMs is introduced and made available in the accompanying nowcast_lstm Python library, which is now also available in R, MATLAB, and Julia.