# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 2 Related Work
### Informer Model  
The Informer model is a novel time series prediction model designed to bring new solutions to long-term series prediction tasks through its powerful long-term series alignment and processing capabilities. The model significantly reduces the consumption of graphics processing unit (GPU) resources by combining the advantages of the transformer model with the processing power of traditional recurrent neural networks (RNNs), making long-term series prediction tasks more practical and feasible. The architecture and training process of the Informer model involves several key techniques and optimizations, including prob-sparse self-attention, self-attention distillation mechanisms, generative decoders, etc. These techniques and mechanisms work together to make the Informer model perform well in dealing with long-term series prediction problems. Many scholars often combine other deep learning methods to enhance the accuracy and precision of the prediction model. [PERSON] [26] and [PERSON] et al. [27] proposed a new short-term wind power prediction model based on temporal convolutional networks (TCNs) and the Informer model to solve the problem of low prediction accuracy due to the large fluctuation of wind speed in short-term predictions. [PERSON] et al. [28] proposed graph convolutional networks-Informer (GCNs-Informer) to construct the prediction model, which used GCN to establish the relationship between multiple wind turbine arrays and enhance the data correlation. The Informer model was used to extract temporal information from the data and predict the long-term series. [PERSON] et al. [29] proposed a new hybrid optimization forecasting strategy consisting of the similar hours (SHs) method and the Informer model. [PERSON] et al. [30] proposed an algorithm based on variational modal decomposition (VMD) and the enhanced chaotic game optimization (ECGO) algorithm, and an improved solar power forecasting method using the Informer model with a locality-sensitive hashing (LSH) attention mechanism. [PERSON] et al. [31] proposed a hybrid algorithm combining ensemble empirical modal decomposition (EEMD) and the Informer model, in which the parameters of the Informer were optimized using the particle swarm optimization algorithm (PSO). [PERSON] et al. [32] argued that the identification of causal relationships between external variables and loads is an important factor for accurate load forecasting in power load forecasting, and therefore proposed an Informer hybrid forecasting method with improved causal reasoning. The model constructed by [PERSON] et al. [33] abandoned the common recurrent neural network to deal with the time series problem and adopted a sparse self-attention mechanism as the main body of the seq2 seq structure, supplemented by specific input and output modules to deal with the long-range relationships in the time series, effectively using the parallelism advantage of the self-attention mechanism and thus improving the prediction accuracy and prediction efficiency. [PERSON] et al. [34] proposed a long-term structure for long-series time series forecasting (LSTF) based on an improved Informer model combined with the fast Fourier transform (FFT) method. LSTF for long-term structural state trend predictions is referred to as the FFT-Informer model. This method uses FFT to represent the structural state trend characteristics by extracting the magnitude and phase from a certain period of a data series. [PERSON] et al. (2019) proposed an optimization model combining convolutional neural networks (CNNs) and the Informer model to predict the dynamic rise in the temperature of bearings. [PERSON] et al. (2019) proposed a new deep learning network based on an information encoder and the bi-directional long short-term memory (IE-Bi-LSTM), which utilizes the encoder part of the Informer model to capture connections globally, extracts long feature sequences with rich information from multi-channel sensors, and employs an attentional distillation layer to improve computational efficiency. [PERSON] et al.