Stock market forecasting research based on GA-WOA-LSTM
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a hybrid model combining Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Long Short-Term Memory (LSTM) for improved stock market forecasting. The GA-WOA-LSTM model enhances prediction accuracy for financial time series.
Area Of Science
- Financial forecasting and time series analysis.
- Computational intelligence and machine learning applications.
- Economic modeling and market regulation.
Background
- Global financial markets are increasingly complex, necessitating accurate stock market forecasting for investment, regulation, and planning.
- Traditional forecasting models often struggle with the nonlinear dependencies and long-term patterns inherent in financial time series data.
Purpose Of The Study
- To propose and evaluate a novel hybrid prediction model, GA-WOA-LSTM, for enhanced stock market forecasting.
- To leverage the strengths of Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Long Short-Term Memory (LSTM) for superior predictive performance.
Main Methods
- A hybrid model integrating GA for global hyperparameter optimization, WOA for local search refinement, and LSTM for time series modeling.
- LSTM neural networks were utilized for their capability in capturing nonlinear dependencies and long-term patterns.
- Model performance was assessed using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R2 on training and test datasets.
Main Results
- The GA-WOA-LSTM model demonstrated significantly higher predictive accuracy compared to traditional baseline models.
- The proposed model exhibited superior generalization capability on both training and test datasets.
- Key performance metrics (MAE, MAPE, RMSE, R2) indicated the effectiveness of the hybrid approach.
Conclusions
- The GA-WOA-LSTM model offers a robust and effective strategy for financial time series forecasting.
- This research provides valuable insights for practical applications in real-world financial markets.
- The integration of optimization algorithms with deep learning enhances the accuracy and reliability of stock market predictions.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
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.
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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...
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 the equation, is the dependent...
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...