# Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction
## 2 Background  
Time-series prediction has always been a daunting challenge. Scholars in this domain have diligently delved into the intrinsic laws governing time-series data through extensive exploration and research. Consequently, they have amassed a considerable repertoire of prediction methods based on the evolving patterns of this data, broadly categorized into linear and nonlinear prediction methods. During the initial stages, prediction methods heavily leaned towards linear approaches, employing classic algorithms like the exponential smoothing method [1; 2] and the autoregressive integral moving average prediction method [3; 4; 5]. These methods boasted advantages such as simplicity, reduced computational demands, and superior performance in short-term predictions. However, they fell short in capturing the inherent nonlinear relationships within financial time-series data, particularly when tackling long-term predictions. Consequently, they exhibited certain limitations in such scenarios. To overcome the limitations of linear methods, subsequent research proposed the integration of nonlinear models to enhance the comprehension of complex data, thereby giving rise to nonlinear prediction methods. Prominent among these methods are BP neural networks [6; 7; 8], support vector machines, recurrent neural networks [9; 10; 11], generative adversarial networks [12; 13], and reinforcement learning [14; 15; 16]. By employing these methodologies, researchers have achieved a more comprehensive capture of the nonlinear relationships embedded in financial time-series data, leading to relatively accurate prediction outcomes. This direction represents the focal point of future research and the prevailing trend in the field of financial time-series data. In recent years, deep learning has attracted considerable attention from researchers in various fields. Deep learning methods have demonstrated remarkable performance when compared to traditional algorithms in time-series prediction tasks, which have undergone extensive development and widespread application. Of particular note, deep neural networks possess superior capabilities in extracting both linear and nonlinear features, outperforming shallow neural networks in this regard. This advantage enables them to capture underlying patterns that may be overlooked by their shallower counterparts, making them well suited for high-precision prediction tasks [17]. In light of these advancements, this section aims to introduce three primary categories of deep learning models that are particularly suitable for addressing challenges in time-series forecasting. Convolutional Neural Networks (CNNs) represent a class of deep feed-forward neural networks that center around convolution and pooling operations. Originally developed for image recognition in the domain of computer vision [18; 19], CNNs have since demonstrated their versatility in various fields. In 2018, [PERSON] et al. [20] proposed an innovative architecture called Temporal Convolutional Networks (TCNs), a variant of CNNs designed with reduced memory consumption and increased parallelizability. TCNs introduced causal convolution to ensure that future information is not accessed during training, thus mitigating issues related to gradient vanishing and gradient explosion. Additionally, the backpropagation path in TCNs differs from the temporal direction, providing added stability. To address the problem of information loss caused by an excessive number of layers in CNNs, TCNs incorporate residual connectivity, facilitating seamless information transfer across layers within the network. Recurrent Neural Networks (RNNs) are a form of deep learning model introduced by [PERSON] in 1990 specifically designed to capture time-dimensional features. Later, in 1997, [PERSON] et al. [21] extended the RNN architecture, leading to the creation of Bidirectional Recurrent Neural Networks (Bi-RNNs). To address some limitations of RNN models, [PERSON] proposed Long Short-term Memory (LSTM) in 1997 [22]. Subsequently, in 2005, [PERSON] et al.