## 2 Literature Review
### Price Forecasting
#### 2.2.3 Forecasting Methods Based on Deep Learning  
Deep learning techniques have undergone significant advancements across various fields, which have empowered machines to grasp abstract concepts and enhance their capabilities in representational learning. Neural networks have emerged as universal frameworks for dealing with issues in multiple research fields. [PERSON] et al. [17] proposed Recurrent Neural Network (RNNs), which are commonly employed for processing time-series data; however, they have issues with handling the gradient disappearance problem, long-term dependencies, and parallelization. In this regard, gating mechanisms can be utilized to mitigate gradient disappearance and better capture temporal dependencies in LSTM networks. [PERSON] [16] combined wavelet analysis with LSTM to develop financial time-series prediction models that overcome the challenges posed by complex features and non-linear correlations. Comparing LSTM and Gated Recurrent Units (GRUs), the latter exhibit faster convergence due to their fewer number of parameters. Furthermore, CNNs offer efficient parallel data processing, and one-dimensional convolutions can be substituted for RNNs in sequence modeling. [PERSON] [18] has exploited a deep learning model combined with news event data for stock price prediction. Moreover, a convolutional neural network based on event information was established using a text representation in the form of event triples and an attention mechanism. The WaveNet model, introduced by [PERSON] et al. [19], serves as an autoregressive probabilistic model. It employs extended causal convolutions and has been used in audio generation, time-series prediction, and machine translation. [PERSON] et al. [20] introduced the TCN for processing time-series data. [PERSON] et al. [21] developed a discrete dynamic model, utilizing TCN to compute the conditional probabilities for different kinds of stocks. Meanwhile, attention mechanisms can be added to capture the time-varying distribution. As for graph convolutional neural networks, [PERSON] et al. [22] introduced the Spectral Temporal Graph Neural Network (StemGNN) model, which exploits the Graph Fourier Transform (GFT) and Discrete Fourier Transform (DFT) to transform the spatio-temporal domain into the frequency domain, effectively capturing time-space dependencies. [PERSON] et al. [23] proposed the Multimodal Graph Neural Network (MAGNN) for financial forecasting, which integrates intra-modal graph attention and inter-modal attention mechanisms, providing a solution to crucial issues related to financial price forecasting. [PERSON] et al. [24] introduced CauGNN, a model that incorporates neural Granger causal graphs to delineate causal relationships among multiple variables. [PERSON] et al. [25] provided an overview of time-series forecasting based on deep learning. Their work focused on the evolution of hybrid deep models that integrate statistical models with neural networks. [PERSON] et al. [26] discussed the performance of Transformer models in handling time-series data, including assessments of robustness, model dimensions, and seasonal trend decomposition. [PERSON]. [27] supposed that the limitations of Transformers (e.g., quadratic time complexity and high memory consumption) hinder their direct application to long-term time-series prediction. [PERSON] et al. [28] introduced Autoformer based on a deep decomposition architecture and an autocorrelation mechanism, which is an intricate forecasting model for long-term time series that can decompose intricate time series into manageable elements. Next, Informer was used to mitigate the time complexity of the model, handle lengthy sequences, and improve the inference speed. [PERSON] et al. [29] proposed the extension of one-dimensional sequences into a multi-period 2D tensor and encapsulated changes in intra- or inter-periodic variations into rows and columns of the tensor. The algorithm's efficacy covers short- and long-term time-series prediction, a filling task, classification, and anomaly detection.