Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting
Low-carbon and environmentally friendly living boosted the market demand for new energy vehicles and promoted the development of the new energy vehicle industry. Accurate demand forecasting can provide an important decision-making basis for new energy vehicle enterprises, which is beneficial to the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Biomimetics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-7673/8/3/312 |
| _version_ | 1827733726420795392 |
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| author | Xiaoya Ma Mengxiu Li Jin Tong Xiaying Feng |
| author_facet | Xiaoya Ma Mengxiu Li Jin Tong Xiaying Feng |
| author_sort | Xiaoya Ma |
| collection | DOAJ |
| description | Low-carbon and environmentally friendly living boosted the market demand for new energy vehicles and promoted the development of the new energy vehicle industry. Accurate demand forecasting can provide an important decision-making basis for new energy vehicle enterprises, which is beneficial to the development of new energy vehicles. From the perspective of an intelligent supply chain, this study explored the demand forecasting of new energy vehicles, and proposed an innovative SARIMA-LSTM-BP combination model for prediction modeling. The data showed that the RMSE, MSE, and MAE values of the SARIMA-LSTM-BP combination model were 2.757, 7.603, and, 1.912, respectively, all of which are lower values than those of the single models. This study therefore, indicated that, compared with traditional econometric forecasting models and deep learning forecasting models, such as the random forest, support vector regression (SVR), long short-term memory (LSTM), and back propagation neural network (BP) models, the SARIMA-LSTM-BP combination model performed outstandingly with higher accuracy and better forecasting performance. |
| first_indexed | 2024-03-11T01:16:09Z |
| format | Article |
| id | doaj.art-fc7ea96902ec4ac5bc814088ef091d7a |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-11T01:16:09Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-fc7ea96902ec4ac5bc814088ef091d7a2023-11-18T18:29:58ZengMDPI AGBiomimetics2313-76732023-07-018331210.3390/biomimetics8030312Deep Learning Combinatorial Models for Intelligent Supply Chain Demand ForecastingXiaoya Ma0Mengxiu Li1Jin Tong2Xiaying Feng3Department of Logistics Management and Engineering, Nanning Normal University, Nanninng 530023, ChinaDepartment of Logistics Management and Engineering, Nanning Normal University, Nanninng 530023, ChinaDepartment of Economics and Management, Nanning Normal University, Nanninng 530001, ChinaDepartment of Economics and Management, Nanning Normal University, Nanninng 530001, ChinaLow-carbon and environmentally friendly living boosted the market demand for new energy vehicles and promoted the development of the new energy vehicle industry. Accurate demand forecasting can provide an important decision-making basis for new energy vehicle enterprises, which is beneficial to the development of new energy vehicles. From the perspective of an intelligent supply chain, this study explored the demand forecasting of new energy vehicles, and proposed an innovative SARIMA-LSTM-BP combination model for prediction modeling. The data showed that the RMSE, MSE, and MAE values of the SARIMA-LSTM-BP combination model were 2.757, 7.603, and, 1.912, respectively, all of which are lower values than those of the single models. This study therefore, indicated that, compared with traditional econometric forecasting models and deep learning forecasting models, such as the random forest, support vector regression (SVR), long short-term memory (LSTM), and back propagation neural network (BP) models, the SARIMA-LSTM-BP combination model performed outstandingly with higher accuracy and better forecasting performance.https://www.mdpi.com/2313-7673/8/3/312demand forecastingprediction modelingdeep learningintelligent supply chainnew energy vehiclesSARIMA-LSTM-BP model |
| spellingShingle | Xiaoya Ma Mengxiu Li Jin Tong Xiaying Feng Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting Biomimetics demand forecasting prediction modeling deep learning intelligent supply chain new energy vehicles SARIMA-LSTM-BP model |
| title | Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting |
| title_full | Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting |
| title_fullStr | Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting |
| title_full_unstemmed | Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting |
| title_short | Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting |
| title_sort | deep learning combinatorial models for intelligent supply chain demand forecasting |
| topic | demand forecasting prediction modeling deep learning intelligent supply chain new energy vehicles SARIMA-LSTM-BP model |
| url | https://www.mdpi.com/2313-7673/8/3/312 |
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