Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model
Abstract 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 in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2021-09-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-021-00166-4 |
| _version_ | 1818619477648998400 |
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| author | Quanying Lu Shaolong Sun Hongbo Duan Shouyang Wang |
| author_facet | Quanying Lu Shaolong Sun Hongbo Duan Shouyang Wang |
| author_sort | Quanying Lu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-16T17:38:07Z |
| format | Article |
| id | doaj.art-80b03000905a401483413f990c7fe7f4 |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-12-16T17:38:07Z |
| publishDate | 2021-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-80b03000905a401483413f990c7fe7f42022-12-21T22:22:40ZengSpringerOpenEnergy Informatics2520-89422021-09-014S212010.1186/s42162-021-00166-4Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated modelQuanying Lu0Shaolong Sun1Hongbo Duan2Shouyang Wang3Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of SciencesThe School of Management, Xi’an Jiaotong UniversitySchool of Economics and Management, University of Chinese Academy of SciencesInstitute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of SciencesAbstract 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.https://doi.org/10.1186/s42162-021-00166-4Crude oil priceGLMNETBMASpike-slab lassoLSTM |
| spellingShingle | Quanying Lu Shaolong Sun Hongbo Duan Shouyang Wang Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model Energy Informatics Crude oil price GLMNET BMA Spike-slab lasso LSTM |
| title | Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model |
| title_full | Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model |
| title_fullStr | Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model |
| title_full_unstemmed | Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model |
| title_short | Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model |
| title_sort | analysis and forecasting of crude oil price based on the variable selection lstm integrated model |
| topic | Crude oil price GLMNET BMA Spike-slab lasso LSTM |
| url | https://doi.org/10.1186/s42162-021-00166-4 |
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