利用深度学习算法预测印尼国内生产总值(GDP)

S. Sa'adah, Muhammad Satrio Wibowo
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引用次数: 6

摘要

国内生产总值(GDP)的增长是衡量一个国家金融状况稳定性的重要指标。对于GDP的价值可以知道人均的经济状况。特别是在疫情期间,GDP的突发性波动问题需要进一步研究。该解决方案可以使用预测方法进行覆盖。深度学习作为机器学习模式的一种新方法,在本研究中被用于解决GDP预测问题。使用了LSTM和RNN两种深度学习技术的方法,结果表明预测可以很好地拟合实际数据。LSTM体系结构2和RNN体系结构2的准确率在80%到90%之间。基于这一结果,利用该模型来了解一个国家在新冠肺炎灾难下的GDP波动可以带来新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Gross Domestic Product (GDP) in Indonesia Using Deep Learning Algorithm
Growth Domestic Product (GDP) is the important factor to know the stability of financial condition in a country. Regarding into GDP value could be known the economic condition per capita. Especially, during this pandemic situation, GDP need study further about its sudden fluctuation. The solution can be covered using the prediction approach. Deep learning as new method from machine learning schema had been observed in this research to cope the prediction of GDP problem. Two methods of deep learning techniques that were used, LSTM and RNN, shown that the prediction could fit the data actual very well. The accuracy at around 80% until 90% emerge from LSTM architecture 2 and RNN architecture 2. Based on this result, it could bring new perspective to use this model to know the GDP fluctuation in a country even in catastrophe of Covid-19.
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