利用深度学习模拟COVID-19期间进出口汇率收敛因子的世界贸易预测
Simulating Using Deep Learning The World Trade Forecasting of Export-Import Exchange Rate Convergence Factor During COVID-19
摘要：
我们所说的贸易通常是指国家和地区之间的货物交换。国际贸易是经济繁荣指数的晴雨表，每个国家都过度依赖资源，因此国际贸易必不可少。贸易对全球健康危机至关重要，可以拯救生命和生计。通过从国家网站NZ Tatauranga Aotearoa收集名为“COVID-19对贸易的影响”的数据集，我们使用深度学习模型开发了一个关于COVID-19对世界贸易影响的可持续预测流程。在研究中，我们给出了一个180天的贸易预测，其中准确预测了2019冠状病毒病期间每日进出口的起伏。为了实现这一预测，我们收集了2015年1月1日至2021年5月30日所有国家、所有商品和所有运输系统的数据，并恢复了2019冠状病毒病期间未来180天的世界贸易状况。深度学习方法在深度观察领域受到了投资者和研究人员的同等关注。本研究利用长短期记忆模型预测全球贸易。时间序列分析有助于了解给定的资产、证券或经济如何随时间变化。时间序列分析在过去的分析中起着重要的作用，可以得到对未来的不同预测，并且可以观察到某些因素会影响一个特定的变量。通过时间序列，可以观察各种经济变化或贸易影响如何随时间变化。通过审查这些变化，人们可以了解未来应采取的步骤，一个国家可以相应地在进出口方面更加谨慎。从我们的时间序列分析来看，可以说LSTM模型在贸易方面对未来世界进出口形势给出了非常有风度的思考。
英文摘要：
By trade we usually mean the exchange of goods between states and countries. International trade acts as a barometer of the economic prosperity index and every country is overly dependent on resources, so international trade is essential. Trade is significant to the global health crisis, saving lives and livelihoods. By collecting the dataset called "Effects of COVID19 on trade" from the state website NZ Tatauranga Aotearoa, we have developed a sustainable prediction process on the effects of COVID-19 in world trade using a deep learning model. In the research, we have given a 180-day trade forecast where the ups and downs of daily imports and exports have been accurately predicted in the Covid-19 period. In order to fulfill this prediction, we have taken data from 1st January 2015 to 30th May 2021 for all countries, all commodities, and all transport systems and have recovered what the world trade situation will be in the next 180 days during the Covid-19 period. The deep learning method has received equal attention from both investors and researchers in the field of in-depth observation. This study predicts global trade using the Long-Short Term Memory. Time series analysis can be useful to see how a given asset, security, or economy changes over time. Time series analysis plays an important role in past analysis to get different predictions of the future and it can be observed that some factors affect a particular variable from period to period. Through the time series it is possible to observe how various economic changes or trade effects change over time. By reviewing these changes, one can be aware of the steps to be taken in the future and a country can be more careful in terms of imports and exports accordingly. From our time series analysis, it can be said that the LSTM model has given a very gracious thought of the future world import and export situation in terms of trade.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
