# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 4 Experiment and Analysis
### Experiments and Analysis of Results
#### 4.3.2 Comparative Experiments on Single-Predictive Models  
To validate the predictive performance of the supply forecasting method proposed in this paper, a comparative experiment was conducted using a single-prediction model. The enhanced Informer model was compared against several classic time series forecasting models, including the linear auto-regressive integrated moving average (ARIMA) model, the nonlinear support vector regression (SVR) model, the LSTM model, the gated recurrent units (GRUs) model, and the backpropagation neural network (BPNN) model, as well as the original Informer and InformerStack models. This comparison aimed to assess the fitting capability of the proposed supply forecasting model for the logistics transportation supply volume. The results of the comparative experiments between the proposed method and the single-prediction models are presented in Table 5.  
From Table 5, it can be observed that compared to single-prediction models such as ARIMA, SVR, LSTM, GRU, BPNN, Informer, and InformerStack, the proposed method yields smaller MSE and MAE results, demonstrating superior performance in supply forecasting accuracy. To further validate and visually illustrate the predictive performance of the proposed method for supply volume, a visualization of the fitting results from the comparative experiments on the test set is presented. The results of the single prediction model comparison are depicted in Figure 9, where Enhanced_Informer represents the predicted values of the proposed method and the legend includes the corresponding supply volume predictions from the other models, with TRUE denoting the actual supply volume values. In the comparative experiments of single-prediction models, the Enhanced_Informer model demonstrates a superior degree of fit for supply volumes, resulting in a reduced discrepancy between predicted and actual values. Specifically, the ARIMA model primarily captures the linear characteristics of supply volume but performs poorly on nonlinear traits; the proposed method reduces MSE by 73.8% and MAE by 52% compared to the ARIMA model. The support vector regression (SVR) model fits predictions by identifying a plane or \"hyperplane\" in a high-dimensional space of data points. In comparison, the proposed method achieves a reduction of 79.36% in MSE and 59.5% in MAE. When compared to the LSTM and GRU models, which utilize gated units for long-term predictions, the proposed method reduces MSE by 82.24% and 78.58%, respectively, with corresponding reductions in MAE of 60.36% and 57.52%. Additionally, in comparison to the BPNN model, known \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Target** & **Car-num** & **Etth1** \\ \hline \multirow{2}{*}{Improved Informer Model} & MSE & 2.79 & 1.39 \\  & MAE & 1.30 & 0.60 \\ \hline \multirow{2}{*}{ARIMA Model} & MSE & 10.65 & 10.11 \\  & MAE & 2.71 & 2.98 \\ \hline \multirow{2}{*}{SVR Model} & MSE & 13.52 & 1.18 \\  & MAE & 3.21 & 0.94 \\ \hline \multirow{2}{*}{LSTM Model} & MSE & 15.71 & 0.21 \\  & MAE & 3.28 & 0.35 \\ \hline \multirow{2}{*}{GRU Model} & MSE & 13.03 & 0.20 \\  & MAE & 3.06 & 0.34 \\ \hline \multirow{2}{*}{BPNN Model} & MSE & 12.14 & 0.22 \\  & MAE & 2.82 & 0.36 \\ \hline \multirow{2}{*}{Informer Model} & MSE & 6.06 & 0.90 \\  & MAE & 1.89 & 0.82 \\ \hline \multirow{2}{*}{InformerStack Model} & MSE & 4.68 & 0.96 \\  & MAE & 2.05 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of single-prediction model comparison. for its efficacy in extracting nonlinear features, the proposed method results in a 77.02% decrease in MSE and a 53.9% decrease in MAE. In the experiments involving long-sequence forecasting algorithms, the proposed method enhances data representation capabilities by incorporating multidimensional feature engineering, introducing GCN for extracting spatial features of transport routes, and integrating STCN and LSTM modules to bolster long-sequence dependency.