The Analysis of Enterprise Improvement in Global Commodity Price Prediction Based on Deep Learning

Abstract

The article expects to solve the traditional econometric statistical model, shallow machine learning algorithm, and many limitations in learning the nonlinear relationship of related indicators affecting commodity futures price trend. This article proposes a neural network commodity futures price prediction model by the mixture of convolutional neural networks (CNN) and gated recurrent unit (GRU). Firstly, the dimension reduction algorithm of multidimensional data by principal component analysis (PCA) is used. Through linear transformation, the original variables with correlation are transformed into a set of new linear irrelevant variables, and the high-dimensional time series data of commodity futures are reduced. Secondly, the variable features are extracted from the CNN network module in the CNN-GRU model, and the GRU network module learns the periodicity and trend of the original data. Finally, the full connection layer outputs the forecast results of commodity futures price.