Forecasting analysis of automobile sales based on VMD-CNN-GRU-LSTM composite model
To address the complex characteristics about time series data of automobile sales,such as seasonality,non-linearity,and non-stationarity,this study proposed a forecasting model that integrated Variational Mode Decomposition(VMD)with Conv-olutional Neural Network(CNN),Gated Recurrent Units(GRU),and Long Short-Term Memory(LSTM)networks.The proposed method began by decomposing the automobile sales data using VMD,followed by the extraction of key features through the appli-cation of CNNs.Subsequently,the model employed GRU and LSTM networks to capture the temporal dependencies inherent in automobile sales data.Experimental results demonstrated that the proposed approach exhibited superior forecasting performance.
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