论文标题
预测外汇率：传统计量经济学，当代机器学习与深度学习技术之间的多元比较分析
Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning Techniques
论文作者
论文摘要
在当今的全球经济中，预测宏观经济参数（例如外国）汇率或至少正确估算趋势的准确性对于未来的任何投资都至关重要。最近，证明将基于计算智能的技术用于预测宏观经济变量。本文试图提出一种多变量时间序列方法来预测汇率（USD/INR），同时比较三种多元预测建模技术的性能：矢量自动回归（一种传统的计量经济学技术），支持向量机器（一种现代机器学习技术），以及一种现代的Neural网络，以及一种现代学习技术）。从1994年4月到2018年12月，我们使用的几个宏观经济变量使用了每月的历史数据，以预测USD-INR外汇率。结果清楚地表明，SVM和RNN（长期短期记忆）的当代技术优于广泛使用的自动回归方法。具有长短期内存（LSTM）的RNN模型提供了最大精度（97.83％），其次是SVM模型（97.17％）和VAR模型（96.31％）。最后，我们对用于预测的变量的相关性和相互依赖性进行了简要分析。
In todays global economy, accuracy in predicting macro-economic parameters such as the foreign the exchange rate or at least estimating the trend correctly is of key importance for any future investment. In recent times, the use of computational intelligence-based techniques for forecasting macroeconomic variables has been proven highly successful. This paper tries to come up with a multivariate time series approach to forecast the exchange rate (USD/INR) while parallelly comparing the performance of three multivariate prediction modelling techniques: Vector Auto Regression (a Traditional Econometric Technique), Support Vector Machine (a Contemporary Machine Learning Technique), and Recurrent Neural Networks (a Contemporary Deep Learning Technique). We have used monthly historical data for several macroeconomic variables from April 1994 to December 2018 for USA and India to predict USD-INR Foreign Exchange Rate. The results clearly depict that contemporary techniques of SVM and RNN (Long Short-Term Memory) outperform the widely used traditional method of Auto Regression. The RNN model with Long Short-Term Memory (LSTM) provides the maximum accuracy (97.83%) followed by SVM Model (97.17%) and VAR Model (96.31%). At last, we present a brief analysis of the correlation and interdependencies of the variables used for forecasting.
