Purpose

According to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model.

Design/methodology/approach

First, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model.

Findings

The model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price.

Originality/value

This paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.

中文翻译：

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基于LSTM网络和GM（1,1）模型的原油价格预测

目的

针对原油价格预测的问题，本文的目的是提出一种基于经验模式分解，长短期记忆网络和GM（1,1）模型的多步预测方法。

设计/方法/方法

首先，采用经验模式分解方法将原油价格序列分解为具有不同频率的几个组成部分。然后，根据特定的周期性和其他属性对每个子序列进行分类和合成，以获得具有不同显着特征的多个分量。最后，将所有组件替换为合适的预测模型以进行拟合。构建具有不同参数的LSTM模型以预测特定的组件，这些组件近似地分别代表短期市场扰动和长期影响。构建了滚动GM（1,1）模型，以模拟一个代表油价发展趋势的序列。最终，将从预测模型获得的所有结果进行汇总，以评估模型的性能。

发现

该模型分别用于模拟每日，每周和每月的WTI原油价格序列。结果表明，该模型具有较高的预测精度，特别是在表示频率较低的长期影响的序列方面。GM（1,1）模型在适应原油价格趋势方面具有出色的性能。

创意/价值

本文将GM（1,1）模型与LSTM网络相结合，以预测WTI原油价格序列。根据不同序列的不同特征，构建了合适的预测模型来模拟各分量。