论文标题
使用多个计量经济学和LSTM神经网络模型对棉花价格波动的全面研究
A comprehensive study of cotton price fluctuations using multiple Econometric and LSTM neural network models
论文作者
论文摘要
本文提出了一种新的连贯模型，以使用计量经济学和长期术语记忆神经网络（LSTM）方法进行全面研究。我们称为简单的棉花价格趋势，然后在结构方法（ARMA），马尔可夫开关动态回归，同时方程式系统，Garch家族程序和人工神经网络中提出了猜想，从而决定了棉花价格趋势持续时间1990-2020的特征。可以确定，在结构方法中，最好的过程是Markov切换估计值AR（2）。基于MS-AR程序，它得出的结论是，从减少趋势变为增加的趋势比反向模式更为重要。同时的方程式系统根据面积棉，增值和真实的棉花价格研究了三个程序。最后，使用GARCH家族的预测是最合适的模型，在LSTM神经网络中，结果显示了训练测试方法的准确预测。
This paper proposes a new coherent model for a comprehensive study of the cotton price using econometrics and Long-Short term memory neural network (LSTM) methodologies. We call a simple cotton price trend and then assumed conjectures in structural method (ARMA), Markov switching dynamic regression, simultaneous equation system, GARCH families procedures, and Artificial Neural Networks that determine the characteristics of cotton price trend duration 1990-2020. It is established that in the structural method, the best procedure is AR (2) by Markov switching estimation. Based on the MS-AR procedure, it concludes that tending to regime change from decreasing trend to an increasing one is more significant than a reverse mode. The simultaneous equation system investigates three procedures based on the acreage cotton, value-added, and real cotton price. Finally, prediction with the GARCH families TARCH procedure is the best-fitting model, and in the LSTM neural network, the results show an accurate prediction by the training-testing method.
