数据特征驱动的新能源汽车月度销量二次分解集成预测

doi:10.16381/j.cnki.issn1003-207x.2023.2035

Abstract

Abstract:

Driven by policy support， technological progress and market demand， China's new energy vehicle market has shown a strong momentum of development， and the rapid development of the new energy vehicle market has attracted wide attention， and its sales forecast is crucial for enterprises and governments. At present， the sales forecast of new energy vehicles is mainly based on annual sales. Considering that the sales volume of new energy vehicles is susceptible to various factors， the monthly sales data has the phenomenon of aliasing data characteristics， which makes multi-step forecasting a challenge.In view of the complexity， seasonality， trend and other characteristics of monthly sales timing data of new energy vehicles， based on the modeling idea of “disintegration-integration”， a secondary disintegration-integration framework for monthly sales forecasting of new energy vehicles is constructed. First， the original sequence is decomposed by the ensemble empirical mode decomposition （EEMD） algorithm to extract the trend item data features. Secondly， the algorithm STL （Seasonal and Trend decomposition using Loess） is used to extract the seasonal terms and residual terms from the detrended item sequence. Then， the Logistic regression， autoregressive integrated moving average model （ARIMA） and deep Extreme Learning machine （DELM） models are respectively used to predict the trend term， seasonal term and residual term. Finally， the forecast results of each item are integrated to get the final forecast results.The empirical analysis based on China's new energy vehicle sales data shows that the MAPE of the proposed quadratic decomposition integrated model in the three-step， six-step and twelve-step forecasting is 5.80%， 6.10% and 7.34%， respectively， which is more than 15% lower than the benchmark model， and the improvement effect is significant. In the future， for the prediction of complex time series data， the corresponding characteristics of time series can be fully considered and extracted for modeling prediction.

Key words: new energy vehicle, ensemble empirical mode decomposition, multistep prediction, quadratic decomposition-ensemble method

CLC Number:

F272.1

Fang Wang,Ankun Zhao,Lean Yu. Quadratic Decomposition-ensemble Method for Multi-step Forecast of Monthly Sales Volume of New energy Vehicles[J]. Chinese Journal of Management Science, 2026, 34(11): 361-369.

Figures/Tables 7

References 42

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模型类型	模型	MAPE（%）	RMSE
单模型	ARIMA	30.35	119405.16
单模型	DELM	25.89	115382.89
混合模型	STL-DELM	22.82	88672.87
	STL-Logistic-DELM	19.84	74644.39
	EEMD-DELM	19.76	73773.00
	Logistic-DELM	19.42	84145.79
EEMD-STL二次分解模型	EEMD-STL-Logistic/ARIMA/DELM	5.80	30674.27

模型类型	模型	MAPE（%）	RMSE
单模型	ARIMA	43.35	185337.14
单模型	DELM	37.47	163836.84
混合模型	STL-DELM	29.16	130331.64
	EEMD-DELM	28.93	128935.81
	STL-Logistic-DELM	20.72	89934.26
	Logistic-DELM	19.86	74209.33
EEMD-STL二次分解模型	EEMD-STL-Logistic/ARIMA/DELM	6.10	34528.85

模型类型	模型	MAPE（%）	RMSE
单模型	ARIMA	53.53	203995.03
单模型	DELM	49.87	218202.42
混合模型	STL-DELM	38.66	154678.18
	EEMD-DELM	31.97	183536.88
	STL-Logistic-DELM	21.34	72712.26
	Logistic-DELM	21.26	80366.28
EEMD-STL二次分解模型	EEMD-STL-Logistic/ARIMA/DELM	7.34	41435.26

Quadratic Decomposition-ensemble Method for Multi-step Forecast of Monthly Sales Volume of New energy Vehicles

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