# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 4 Experiment and Analysis
### Experiments and Analysis of Results  
#### 4.3.1 Multidimensional Feature Engineering of Data  
The supply volume, like the demand volume, exhibits temporal characteristics, with freight rates serving as a key influencing factor for changes in supply volume. The temporal evolution of supply volume and freight rates is illustrated in Figure 6, which shows the evolution of supply and freight rates, with (a) a chart of time series changes in supply and (b) a chart of changes in the chronology of freight costs, where the dashed line represents the trend line. Overall, the supply volume shows an upward trend over time, with a recurring spiral seasonality. In contrast, freight rates remain relatively stable over time, exhibiting a slight upward trend punctuated by sharp fluctuations on specific days each year. These fluctuations are closely related to factors such as transportation industry dynamics and unforeseen social events.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Descriptive** & **Value** \\ \hline seq\_len & Sequence length & 160 \\ label\_len & Label length & 64 \\ pred\_len & Predicted length & 64 \\ freq & Frequency & d \\ enc\_in & Encoder input size & 7 \\ dec\_in & Decoder input size & 7 \\ c\_out & Output & 7 \\ d\_model & Model size & 512 \\ n\_heads & Attention span & 8 \\ e\_layers & Encoder layers & 2 \\ d\_layers & Decoder layers & 1 \\ s\_layers & Stack encoder layers & \([3,2,1]\) \\ d\_ff & Fcn dimension & 2048 \\ factor & Prob atten factor & 5 \\ distill & Distillate & True \\ attn & Attention & prob \\ embed & Embedding & timeF \\ train\_epochs & Number of trainings & 100 \\ batch\_size & Batch size & 32 \\ learning\_rate & Learning rate & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model parameter settings.  
Figure 6: Evolution of supply and freight rates. (**a**) Chart of time series changes in supply and (**b**) chart of changes in the chronology of freight costs.  
According to the Pearson correlation coefficient, the correlation coefficient between the supply quantity and each influencing factor is calculated to assess the linear relationship between the different factors and supply quantity. The Pearson correlation coefficient calculation results of the supply indicators are plotted in Figure 7, with a darker color representing a stronger correlation.  
According to the analysis of supply correlation characteristics in Table 4, the correlation coefficient between supply (Car-num) and demand (Order-num) is 0.85, meaning there is a very strong correlation, freight and time evolution characteristics, and a supply correlation. This paper will examine the existence of a correlation between the influencing factors and the input data of the supply to construct the feature engineering in Figure 8 to enhance the expression of the input data and improve the supply prediction model's performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date** & **Weekends** & **Quarter** & **Year** & **Month** & **Cost** & **Order-Num** & **Car-Num** \\ \hline
2018/1/1 & 1 & 1 & 1 & 1 & 9235 & 45 & 1 \\
2018/1/2 & 1 & 1 & 2 & 1 & 15,205 & 112 & 1 \\
2018/1/3 & 1 & 1 & 3 & 1 & 11,350 & 91 & 1 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
2023/5/12 & 1 & 2 & 132 & 5 & 118,018 & 1053 & 20 \\
2023/5/13 & 0 & 2 & 133 & 5 & 103,078 & 889 & 25 \\
2023/5/14 & 0 & 2 & 134 & 5 & 102,251 & 875 & 47 \\ \hline \hline \end{tabular} Data sources: the authors have compiled the data based on the logistics supply on the route of H Logistics transporting from the city of W to the city of K (April 2018-August 2022).  
\end{table}
Table 4: Example of input data feature engineering.  
Figure 8: Multidimensional feature engineering of data.  
Figure 7: Pearson correlation analysis chart.