# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 3 Problem Description and Model Design
### Principles of Informer Model Timing Prediction Algorithm
\[X_{j+1}^{t}=MaxPool(ELU(Convld([X_{j}^{t}]_{AB}))) \tag{6}\]  
where \(X_{j+1}^{t}\) denotes the distillation operation from layer \(j\) to layer \(j+1\), \(\left[X_{J}^{T}\right]_{AB}\) denotes the attention module and the basic variables of sparse attention, _Maxpool_ represents the maximum pooling process with a step size of two, \(ELU\) represents the activation function, and _Convld_ represents the one-dimensional convolution. The distillation technique incorporates one-dimensional convolution and max pooling. After the sequence data have been processed by the multi-head sparse attention mechanism, it is fed directly into the distillation layer, which reduces the network parameters and emphasizes important features. This process involves dimensionality reduction and lowering memory consumption, scaling the output dimensional variables to half of their original length and temporal range. This operation is repeated until the final output is achieved, after which the results are passed to the subsequent multi-head attention module for feature exchange. Specifically, the distillation layer utilizes one-dimensional convolution and max pooling to scale down the dimensions, thereby enhancing the model's robustness and reducing its memory usage. The construction of this process within the encoder is illustrated in Figure 1.  
* Generative Decoder  
The decoder of the Informer model includes fully connected layers, full attention layers, and multi-head probability sparse self-attention layers. To prevent auto-regression from interfering with training effectiveness, the decoder input is a masked sparse multi  
Figure 1: Self-attentive distillation mechanism. head attention mechanism consisting of a target placeholder sequence and partial historical sequences. The predicted length of the long sequence is equal to the length of the target placeholder sequence. Placeholder sequence positions are filled with zeros for long-sequence prediction to avoid influence from information not yet predicted on the current prediction point. The input sequence \(X^{t}_{de}\) of the decoder at moment \(t\) consists of two parts; the first part is a reference to the historical sequence \(X^{t}_{token}\) and the second part is a predictive sequence \(X^{t}_{0}\) used to mask the future characterizing factors, as shown below. \[X^{t}_{de}=Fusion[Concat(X^{t}_{token},X^{t}_{0})]\in R^{(L_{ token}+L_{y})d_{\text{mod}}} \tag{7}\]  
where \(X^{t}_{token}\) represents the historical sequence, i.e., the output dimension of the encoder, \(X^{t}_{0}\) represents the target placeholder sequence, which is uniformly set to 0, _Concat_ represents the sequence concatenation function, which indicates the splicing of the historical sequence and the target placeholder sequence, and _Fusion_ is the feature fusion operation. The generative decoder of the Informer model employs a generative inference method for decoding. It utilizes multi-head attention to handle intermediate results from the encoder output, adjusts the output data dimensionality through fully connected layers, and applies inverse normalization operations to the output data. During the training process, the entire prediction results are generated at once, eliminating the need to rely on the output from the previous step. This significantly enhances the computational efficiency of decoding.