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Enhanced stock market forecasting using dandelion optimization-driven 3D-CNN-GRU classification.

B N Jagadesh¹, N V RajaSekhar Reddy², Pamula Udayaraju³

¹School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India. nagajagadesh@gmail.com.

Scientific Reports

|September 8, 2024

Summary

This summary is machine-generated.

This study introduces advanced machine learning for stock market forecasting, achieving 99.14% accuracy with a novel 3D-CNN-GRU model. The approach uses optimized feature selection and hyperparameter tuning for robust market prediction.

Keywords:

Blood coagulation algorithm Convolutional neural network Dandelion optimization algorithm Gated recurrent unit Stock market Wavelet transform

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

* Computational Finance
* Artificial Intelligence
* Data Science

Background:

* Traditional statistical models are insufficient for current market prediction needs.
* Global interest in market prediction drives adoption of advanced technologies.
* Machine learning and deep learning offer powerful tools for financial forecasting.

Purpose of the Study:

* To explore machine learning and deep learning for stock market forecasting.
* To propose a comprehensive methodology including feature selection, data preprocessing, and classification.
* To develop and evaluate a novel hybrid deep learning model for enhanced prediction accuracy.

Main Methods:

* Wavelet transform for data cleaning and noise reduction.
* Dandelion Optimization Algorithm (DOA) for efficient feature selection.
* A novel hybrid 3D-CNN-GRU model for stock market data analysis.
* Blood Coagulation Algorithm (BCA) for hyperparameter tuning.

Main Results:

* Achieved a remarkable prediction accuracy of 99.14%.
* Demonstrated robustness and efficacy in stock market forecasting.
* Identified key features using the Dandelion Optimization Algorithm.

Conclusions:

* The 3D-CNN-GRU hybrid model shows significant promise for stock market forecasting.
* Model performance is enhanced through optimized feature selection and hyperparameter tuning.
* Future research should explore broader datasets and varied financial contexts for generalizability.