# Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction
## 1 Implication and Contributions of the Research  
Time-series data prediction has perennially occupied a position of paramount importance, manifesting widespread applications across diverse domains, such as finance, weather forecasting, traffic planning, and sales forecasting. In the financial sector, the prediction of time-series data serves as the bedrock for investment decisions. Investors heavily lean on precise forecasts of time-series data, encompassing variables like stock prices, exchange rates, and commodity prices to formulate astute buying and selling strategies. Concurrently, governmental and regulatory bodies also utilize accurate time-series data to vigilantly monitor the stability of the financial markets. In meteorology, the prediction of time-series data assumes critical significance in the accurate forecasting of weather patterns, climate changes, and the onset of natural disasters. Within the domain of traffic planning, the prediction of traffic flow is instrumental in aiding urban planners to adeptly manage traffic congestion and enhance overall traffic efficiency. Equally vital is sales forecasting, which empowers retailers to ascertain optimal inventory requirements, ensuring timely product supply while minimizing inventory costs. The challenges associated with feature selection in time-series data primarily stem from the unique characteristics of such data. Time-series data exhibit temporal correlations, sequence patterns, and seasonality, often making traditional feature selection methods lesssuitable. Dealing with time-series data requires consideration of various complex factors, including the influence of historical information on current observations, the extraction of temporal features, and addressing issues such as missing values and noise. Consequently, developing feature selection methods tailored for time-series data is a crucial research area with the goal of constructing more precise, efficient, and interpretable time-series prediction models. Against this backdrop, this paper introduces a pioneering innovation designed to improve the efficacy of feature selection in time-series data prediction. We leverage an ensemble learning approach that seamlessly integrates five distinct feature selection methods. This integrative framework aims to redress the limitations inherent in various feature selection methods, consequently yielding more precise and resilient feature selection outcomes. The chosen five feature selection methods span diverse dimensions, each endowed with its distinct strengths. Through the amalgamation of these methods, our objective was to comprehensively capture information and patterns within time-series data, thereby enhancing the performance and resilience of the prediction model. The specific innovations and contributions of this paper are outlined as follows:  
1. This paper introduces a feature selection method grounded in ensemble learning. It furnishes a formal definition of the application of ensemble learning to feature selection and, adhering to the principle of \"good but different\", identifies five feature selection methods for integration. The weights of these methods were determined through K-fold cross-validation when applied to specific datasets. This ensemble approach considers the outcomes of multiple feature selection methods, consolidating them into a numerical outcome. This process aids in mitigating potential biases in traditional methods, thereby enhancing the accuracy and comprehensiveness of feature selection;
2. This paper deploys an LSTM model, incorporating features selected through ensemble learning and those identified by five different feature selection methods as inputs to the model. A series of experiments was conducted to validate the effectiveness of this approach. Through the practical applications in time-series prediction tasks, the paper presents concrete data and results to demonstrate the performance and efficacy of the proposed feature selection method in real-world scenarios.