In recent years, the crude oil market has entered a new period of development and the core influence factors of crude oil have also been a change. Thus, we develop a new research framework for core influence factors selection and forecasting. Firstly, this paper assesses and selects core influence factors with the elastic-net regularized generalized linear Model (GLMNET), spike-slab lasso method, and Bayesian model average (BMA). Secondly, the new machine learning method long short-term Memory Network (LSTM) is developed for crude oil price forecasting. Then six different forecasting techniques, random walk (RW), autoregressive integrated moving average models (ARMA), elman neural Networks (ENN), ELM Neural Networks (EL), walvet neural networks (WNN) and generalized regression neural network Models (GRNN) were used to forecast the price. Finally, we compare and analyze the different results with root mean squared error (RMSE), mean absolute percentage error (MAPE), directional symmetry (DS). Our empirical results show that the variable selection-LSTM method outperforms the benchmark methods in both level and directional forecasting accuracy.

中文翻译：

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基于变量选择-LSTM集成模型的原油价格分析与预测

近年来，原油市场进入新的发展时期，原油的核心影响因素也发生了变化。因此，我们为核心影响因素的选择和预测开发了一个新的研究框架。首先，本文采用弹性网正则化广义线性模型（GLMNET）、尖刺-板套索法和贝叶斯模型平均法（BMA）对核心影响因素进行评估和选择。其次，为原油价格预测开发了新的机器学习方法长短期记忆网络（LSTM）。然后是六种不同的预测技术，随机游走 (RW)、自回归集成移动平均模型 (ARMA)、埃尔曼神经网络 (ENN)、ELM 神经网络 (EL)、沃尔维特神经网络 (WNN) 和广义回归神经网络模型 (GRNN)被用来预测价格。最后，我们比较和分析了均方根误差（RMSE）、平均绝对百分比误差（MAPE）、方向对称性（DS）的不同结果。我们的实证结果表明，变量选择-LSTM 方法在水平和方向预测精度方面均优于基准方法。