's Local Linear Embedding Dimensionality Reduction (LLE) algorithm is chosen to reduce the dimensionality of the factors affecting the stock price and the dimensionality-reduced data are used as a new input variable to the BP neural network for stock price prediction [5]. Another commonly used machine learning algorithm is Support Vector Machine (SVM). SVM avoids the problem of overfitting and improves the prediction ability of out-of-sample data. Stock market data have the characteristics of high noise and complex dimensions, while artificial neural networks often show inconsistency in the prediction of noise data. SVM is more suitable for financial time series data prediction [6]. [PERSON] introduced the use of SVM and ANN as prediction algorithms and raised challenges such as time constraints, current scenarios, data limitations, and cold start issues [7]. Random Forest (RF) is an ensemble learning method primarily used for classification and regression tasks. Although it was not originally designed for time series prediction, with appropriate adjustments and feature engineering, random forests can also be effectively applied to time series prediction problems. [PERSON] proposed a hybrid model that combines the Random Forest (RF) model with the mean generation function model, which outperforms the original model based on selected prediction accuracy indicators in terms of peak-valley performance in highly volatile data [8]. Transformer-based models have shown excellent performance in the TSF field, and the latest Mamba has been proven to outperform Transformer in many aspects, not only with strong performance but also by reducing memory and computational overhead. Large Language Models (LLMs) have been applied in many fields and have developed rapidly in recent years. [PERSON]'s experiment showed that Mamba exhibits remarkable potential to outperform Transformer in TSF tasks [9]. As a classic machine learning task, TSF has recently been boosted by LLMs. [PERSON]'s study shows that LLMs perform well in predicting time series with clear patterns and trends but face challenges in the absence of periodic datasets. He also proves Mamba shows great potential to surpass Transformer in TSF tasks [10]. Multi-variate time series prediction is a persistent challenge in various disciplines. On this issue, [PERSON] proposed MSGNet, an advanced deep learning model aimed at using frequency domain analysis and adaptive graph convolution to capture intersequence correlations that vary across multiple time scales [11]. An ANN is a representative method that is applicable to all short-term, recent, and long-term data. The very first generation of ANNs had single-layer computational neurons, which made them capable of handling some linearly separable problems. The second generation of ANNs used multiple hidden layers to replace the original single feature layer in the perception and used the Backpropagation (BP) algorithm to calculate network parameters. But an increasing number of hidden layers and neurons require more computing resources. Meanwhile, the explainability becomes more difficult to ensure. Known as third-generation ANNs, SNNs can be more energy efficient than traditional artificial neural networks because they use spike-based signaling, which requires less energy to transmit than continuous signals. SNNs can also use the timing of spikes to process temporal information, such as sequences of events, so they have a greater advantage in TSF problems theoretically. Shallow-level machine learning algorithms such as SVM and BP neural networks have great limitations in dealing with complex and high-dimensional data, and there are many problems, such as the disaster of dimensionality and inefficient feature representation [12]. Deep learning is a representation learning method with multi-level representation, which is obtained by superimposing multi-layer simple but non-linear modules. Each module converts the representation of each layer (starting from the input layer) into a more abstract representation of the next layer.