# Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency Domain
## 2 Related Works  
In recent years, traffic flow prediction methods based on deep learning have received more and more attention, and numerous new methods have emerged that build upon classical deep learning algorithms. The most basic methods, such as the LSTM [21] and GRU [22] models based on RNN architectures, are widely used in traffic flow prediction problems. Recent studies have focused on solving the gradient problem associated with LSTM and GRU models, and the problem of slow training speed, for example using effective attention mechanisms. Attention mechanisms were proposed to improve the accuracy of sequence-to-sequence (Seq2 Seq) models [4] by assigning weights to features at different time steps. This was achieved by calculating similarity with a given metric (Key) to enhance the feature extraction capabilities of the model. For example, MTGNN [23] attempts to model multivariate time series through an attention mechanism operating over both temporal and spatial dimensions. [PERSON] et al. [24] establish the spatial-temporal correlation of sequences by combining convolutional gating recursive units with attention mechanisms. [PERSON] et al. [25] use attention mechanisms to model different series at adjacent intersections, known as sequential slots. They extract features from the upstream and downstream sequence slots of each target location, and then use the attention mechanism to assign weights to them. [PERSON] et al. [26] adopted LSTM in combination with the attention mechanism to extract time domain features and used convolutional layers to extract spatial features. Thanks to recent breakthroughs in applying the Transformer structure [27] to a wide range of deep learning tasks, its core module named the self-attention mechanism, has become an effective improvement on the standard attention mechanism. Unlike standard attention mechanisms, the similarity indicator (Key) used to support self-attention mechanisms is derived from a linear transformation of the input, hence the adoption of the term \"self\" to denote this class of attentional algorithms. [PERSON] et al. proposed SATP-GAN [28], which is based on self-attention mechanisms and generative adversarial networks (GAN). Their approach uses self-attention instead of an RNN structure to extract sequential time patterns and relies on reinforcement learning methods to adjust model parameters. [PERSON] et al. [29] proposed a Traffic Transformer model that uses global and local encoders to improve transformers for traffic flow prediction problems. The latest time series forecasting model, Autoformer [19], has also achieved excellent performance on traffic flow dataset. This model incorporates a series-level autocorrelation mechanism based on autocorrelation theory to replace the self-attention mechanism, with the goal of capturing periodic information across the sequence more effectively. In addition to the above methods, graph neural networks are also commonly used for traffic flow modeling. Graph neural networks are deep learning methods for processing graph data structures. They are better suited to describing the spatial relationships of roads than convolutional networks; the latter can only handle spatial relationships in Euclidean spaces represented by two-dimensional matrices or raster images, while graph neural networks [29] can represent non-Euclidean pairwise relationships in road networks. Through prior knowledge, self-learning, and other methods, it is possible to obtain a graph structure adjacency matrix between traffic intersections. This tool can be used to describe the degree of correlation between different intersections to explicitly model spatial relationships associated with traffic flow data. [PERSON] et al. [30] propose a graph convolutional recursive network method that combines graph convolution with recurrent neural networks. [PERSON] et al. [31] propose a graph convolutional neural network with a gating mechanism, which preserves the ability of the model to capture long-term temporal correlations.