使用基于森林子集的随机特征选择和带有注意力机制的BiGRU的两阶段预测模型:应用于股票指数
Shafiqah Azman1, Dharini Pathmanathan1,2,3, Vimala Balakrishnan4,5
1Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia.
PloS one
|May 9, 2025
概括
本研究引入了一种用于股票价格预测的新型两阶段机器学习模型. 改进后的模型显著提高了金融专业人士的预测准确性,从而导致更好的投资决策.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 金融预测 金融预测
背景情况:
- 股票市场数据表现出多样性和波动性,使得准确的价格预测具有挑战性.
- 市场的非静止性质受到金融资产,经济因素和参与者行为之间的复杂相互作用的影响.
- 现有的预测方法在与股票价格数据固有的波动性和相互依赖性作斗争.
研究的目的:
- 开发一个强大的机器学习模型,以提高股票价格预测的准确性和可靠性.
- 引入一种新的两阶段预测方法,将特征选择和高级深度学习结合起来.
- 为金融专业人员提供改进的工具,以数据驱动的投资决策.
主要方法:
- 一个两阶段模型,包含基于随机森林子集 (RFS) 的特征选择与k倍交叉验证.
- 选择八个关键预测指标,包括价格,体积,变化和振幅指标.
- 使用带有注意力机制的双向封闭循环单元 (BiGRU-AM) 进行每日开盘价格预测.
主要成果:
- 拟议的RFS-BiGRU-AM模型在十个不同的股票指数中显示出优异的预测性能.
- 对12个基准模型的性能进行了验证,使用了根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 等指标.
- 与现有方法相比,该模型在预测股票开盘价格方面取得了更高的准确性和可靠性.
结论:
- 开发的两阶段模型为股票价格预测能力提供了显著的进步.
- 预测准确度的提高使金融专业人员能够做出更明智的投资决策,减轻风险.
- 这项研究强调了将高级功能选择与深度学习相结合,用于金融市场分析的潜力.
相关概念视频
Quantifying and Rejecting Outliers: The Grubbs Test
1.3K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.3K
Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
Multiple Regression
2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Survival Tree
42
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
42
Regression Analysis
5.5K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.5K
End Point Prediction: Gran Plot
179
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
179


