Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Improving Predictions of Stock Price with Ensemble Learning

Authors
N. Siva1, B. Venkata Sivaiah2, *, P. Vallusha Nikkam3, Varshith Volliboina3, Dommaraju Hema Sai3, Kotala Pushpalatha3
1Assistant Professor, Department of AIML, Annamacharya Institute of Technology and Sciences, Rajampet, AP, India
2Assistant Professor, Department of Data Science, Mohan Babu University, Tirupati, India
3UG Scholar, Dept. Of CSSE., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College, Tirupati, India
*Corresponding author. Email: siva.bheem@hotmail.com
Corresponding Author
B. Venkata Sivaiah
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_53How to use a DOI?
Keywords
Stock Price Prediction; Recurrent Neural Network; Long-Short Term Memory; One dimensional Convolutional Neural Network; Ensemble Approach
Abstract

In today’s financial landscape, accurate stock price forecasting is crucial for informed decisions. This solution leverages machine learning and data science advancements to offer a comprehensive platform for interactive analysis and custom model training. With a user-friendly Streamlit interface, users can explore and forecast stock movements, choosing from models like LSTM, RNN, Conv1D, and ensemble approaches. Modular functions support flexible model customization, including RNNs, LSTMs, and Conv1Ds. An ensemble approach combines multiple models for enhanced accuracy. Seamlessly integrating data retrieval, preprocessing, model training, and visualization, users gain actionable insights into market trends and future predictions. Interactive Plotly visualizations enable deep historical data analysis to support investment strategies. This solution is a versatile tool for both interactive analysis and custom model development in stock market navigation.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_53How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - N. Siva
AU  - B. Venkata Sivaiah
AU  - P. Vallusha Nikkam
AU  - Varshith Volliboina
AU  - Dommaraju Hema Sai
AU  - Kotala Pushpalatha
PY  - 2024
DA  - 2024/07/30
TI  - Improving Predictions of Stock Price with Ensemble Learning
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 548
EP  - 558
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_53
DO  - 10.2991/978-94-6463-471-6_53
ID  - Siva2024
ER  -