# Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction
## 4 Experimental Results and Analysis of Feature Selection Based on Ensemble Learning  
### Experimental Preparation  
#### 4.1.1 Initial Feature Set  
To better illustrate the performance enhancement achieved by the ensemble learning-based feature selection method for time-series data prediction models, this paper utilizes financial time-series data to construct the initial feature set for subsequent experiments. Financial time-series data typically encompass multiple features such as stock prices, trading volume, market capitalization, financial indicators, etc. Financial time-series data are characterized by their richness in information, high dimensionality, temporal nature, and real-time aspects. These characteristics render them well suited for applying ensemble learning methods to feature selection, thereby improving model performance, mitigating risks, and fostering a more profound understanding of financial markets.  
In theory, a broader range of features in the initial feature set implies a more comprehensive coverage of information, leading to more accurate predictive results for the model. It is imperative to choose features that are highly correlated with stock prices or returns to construct the initial feature set for the prediction model. This paper proposes selecting a total of 20 candidate features from three major categories: market-related, trading-related, and market capitalization-related features, with the objective of comprehensively considering various types of features.  
The present study incorporates a set of market-related features, namely, high, open, low, pre_close, pct_change, change, and avg_price, totaling seven features. These features denote the daily stock trading metrics of highest price, opening price, lowest price, previous day's closing price, percentage change, and stock price change, respectively. Market-related features can reflect short-term fluctuations and trends in the market, aiding in capturing instantaneous changes in stock prices.  
For trading-related features, we selected vol_ratio, vol, turn_over, amount, selling, and buying, constituting six features. These features represent volume ratio, trading volume, turnover rate, transaction amount, selling transactions, and buying transactions. Analyzing these trading-related features can provide insights into market activity, trading volume, price fluctuations, and other aspects, contributing to a better understanding of the overall market conditions.  
In terms of market value-related features, the study includes pe, float_mv, total_mv, swing, activity, strength, and attack, totaling seven features. These features signify price-to-earnings ratio, float market value, total market value, amplitude, activity level, strength, and market aggressiveness, respectively. Changes in stock market value may be correlated with investor sentiment, market cycles, and other factors, making market value-related features crucial for predicting stock returns.  
By comprehensively considering these three major categories of features, the initial feature set can encompass information from different aspects, thereby enhancing the predictive accuracy of the model. Table 1 displays the initial feature set selected from these three major categories.