Analysis of the Recall Demand Pattern of Imported Cars and Application of ARIMA Demand Forecasting Model

Jeong, Sangcheon; Park, So‐Hyun; Kim, Seung-chul

doi:10.11627/jkise.2020.43.4.093

Cited by 1 publication

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“…Therefore, researchers often use quantitative forecasting methods for research. For example, some scholars used the autoregressive integrated moving average (ARIMA) model to predict the recall volume of cars for auto importing companies [ 13 ]. Wang et al [ 14 ] used the multivariate grey forecasting model to predict demand for mechanical products.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting

Jin

et al. 2023

Biomimetics

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting

Jin

et al. 2023

Biomimetics

View full text Add to dashboard Cite

show abstract

Analysis of the Recall Demand Pattern of Imported Cars and Application of ARIMA Demand Forecasting Model

Cited by 1 publication

References 0 publications

Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting

Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting

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