Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Regression Analysis

Regression Analysis

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:

Prediction Intervals

Prediction Intervals

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.

First Derivative Test: Problem Solving

First Derivative Test: Problem Solving

Imagine an asset price that crashes to a low point, rebounds sharply as bargain-hunters step in, and then gradually declines. Such behavior can be modeled with a smooth function whose turning points represent locally overvalued and undervalued regions. A convenient example that captures rebound followed by decay is:The high and low points of this curve are identified using the first derivative test, which determines where the function changes from increasing to decreasing or vice versa. To...

Growth Models with Integration: Problem Solving

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Analyzing the critical steps in deep learning-based stock forecasting: a literature review.

PeerJ. Computer science·2024

Same author

Multi level perspectives in stock price forecasting: ICE2DE-MDL.

PeerJ. Computer science·2024

Same author

A new denoising approach based on mode decomposition applied to the stock market time series: 2LE-CEEMDAN.

PeerJ. Computer science·2024

Same author

The use of NARX neural network for modeling of adsorption of zinc ions using activated almond shell as a potential biosorbent.

Bioresource technology·2014

Same author

A fast and adaptive automated disease diagnosis method with an innovative neural network model.

Neural networks : the official journal of the International Neural Network Society·2012

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

2LE-BO-DeepTrade: an integrated deep learning framework for stock price prediction.

Zinnet Duygu Akşehir¹, Erdal Kılıç¹

¹Department of Computer Engineering, Ondokuz Mayis University, Samsun, Turkey.

Peerj. Computer Science

|September 24, 2025

Summary

This study introduces 2LE-BO-DeepTrade, a deep learning framework for stock price prediction. It significantly improves accuracy and trading strategy returns by combining denoising, Bayesian optimization, and a novel trading approach.

Keywords:

2LE-ICEEMDAN Deep learning Mode decomposition Noise reduction Stock price prediction Trading strategy

Related Experiment Videos

Last Updated: Jul 7, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Area of Science:

* Computational Finance
* Machine Learning
* Financial Forecasting

Background:

* Stock market prediction is complex due to inherent noise and volatility.
* Existing deep learning models often struggle with accuracy and effective trading strategy integration.
* Advanced signal processing and optimization techniques are needed for robust financial forecasting.

Purpose of the Study:

* To develop and evaluate an integrated deep learning framework (2LE-BO-DeepTrade) for accurate stock closing price prediction.
* To enhance prediction accuracy by combining advanced denoising, Bayesian optimization, and deep learning models (LSTM, LSTM-BN, GRU).
* To introduce a novel piecewise linear representation (PLR)-based trading strategy to maximize financial returns.

Main Methods:

* Application of 2nd-order Local Exponential Ensemble Empirical Mode Decomposition with Cubic Spline Entropy (2LE-ICEEMDAN) for signal denoising.
* Bayesian optimization (BO) to tune hyperparameters of deep learning models (LSTM, LSTM-BN, GRU).
* Development and implementation of a piecewise linear representation (PLR)-based trading strategy.

Main Results:

* 2LE-ICEEMDAN successfully removed noise, yielding clean intrinsic mode functions (IMFs).
* Bayesian optimization identified optimal models and hyperparameters, significantly boosting prediction accuracy.
* The 2LE-BO-DeepTrade framework demonstrated superior performance over ICE2DE-MDL, reducing RMSE by 94.4%, MAE by 93.6%, and MAPE by 37.4%, while increasing R² by 1.1%.
* The PLR-based trading strategy yielded an average of 66 times more profit than passive investment across tested stocks.

Conclusions:

* The proposed 2LE-BO-DeepTrade framework offers a significant advancement in stock price prediction accuracy.
* Integrated denoising, optimized deep learning, and a specialized trading strategy enhance predictive performance and profitability.
* This approach provides a robust and effective tool for improving stock market forecasting and trading outcomes.