Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting
Problem
New energy vehicle enterprises need accurate demand forecasting as a decision-making basis.
Why it matters
Accurate forecasts provide an important decision-making basis and benefit new energy vehicle industry development.
What the AI did
A SARIMA-LSTM-BP combination model forecasts new energy vehicle demand, achieving lower errors than the single models examined.
Results
RMSE 2.757, MSE 7.603, MAE 1.912, all lower than single models including random forest, SVR, LSTM, and BP.
Impact
Lower forecasting errors than compared models on the studied data; broader adoption not stated.
Record
| Venue | Biomimetics |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Xiaoya Ma, Mengxiu Li, Jin Tong, Xiaying Feng |
| Citations | 21 (OpenAlex, at import time) |
| References | 17 |
| Model or method | SARIMA-LSTM-BP combination model |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/biomimetics8030312 |
| Open access | gold |
| Keywords | demand forecasting, SARIMA-LSTM-BP, new energy vehicles, supply chain, deep learning |
Importance rating
real but narrower advance
Abstract states accurate demand forecasting provides an important decision-making basis for new energy vehicle enterprises and benefits industry development; a real but narrow business forecasting problem, no scale given.
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