论文标题
在COVID-19危机期间，在象征中长期记忆人工神经网络的性能表现
Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis
论文作者
论文摘要
COVID-19大流行证明了对宏观经济变量及时估计的决策者的需求日益增加。先前的婚姻研究论文研究了长期记忆人工神经网络（LSTM）对这种性质进行经济现象的适用性。在这里，比较了LSTM在COVID-19大流行期间的性能，并与动态因子模型（DFM）（一种常用的方法论）进行比较。将三个独立的变量，全球商品出口价值和数量以及全球服务出口，以实际的数据复古和绩效评估的第二，第三和第四季度评估，以及2021年的第一个和第二季度。就平均绝对误差和均等误差而言，LSTM的均等性能更高，并在两分之一的范围内均能表现出更高的表现。叙述和较小的修订。此外，在随附的nowcast_lstm python库中介绍并提供了一种向LSTMS介绍可解释性的方法，该库现在也在R，Matlab和Julia中使用。
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.
