TIME SERIES DAY AHEAD ELECTRICITY PRICE FORECASTING
Objective
The objective of this project is to develop a reliable stock price prediction system using machine learning models like ARIMA, SARIMA, Random Forest, XGBoost, and LSTM. The system will provide accurate forecasts of the Market Clearing Price (MCP) for the Indian Energy Exchange (IEX). A user-friendly interface will be built using Streamlit, allowing users to easily upload historical stock data, and visualize forecast results. Additionally, the system will support model retraining, enabling users to upload new data, select model configurations, and enhance prediction accuracy over time. The project will evaluate and compare the performance of different models, determining the most suitable approach for stock price forecasting. To provide meaningful insights, the system will store and manage prediction results, allowing users to track historical predictions and compare them with actual outcomes. The ultimate goal is to provide actionable insights to users, aiding in informed decision-making by forecasting future stock trends.
Abstract
This project focuses on forecasting stock prices using machine learning techniques applied to time-series data from the IEX website, specifically 15-minute interval data. The system integrates five key algorithms ARIMA, SARIMA, Random Forest, XGBoost, and LSTM to predict stock price trends. Users can upload historical data, adjust model parameters, and select time steps for training, enabling continuous model refinement. Streamlit is used for the front-end interface, providing an interactive platform for data visualization and results storage. The application aims to assist traders, analysts, and investors by delivering accurate stock price predictions based on historical trends, helping users make informed financial decisions. The system also supports model retraining, allowing users to enhance prediction accuracy as new data becomes available. This project combines statistical modeling with machine learning to improve stock price forecasting.
Keywords:
Stock prediction, time-series, ARIMA, SARIMA, Random Forest, XGBoost, LSTM, machine learning, financial forecasting, Streamlit.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE REQUIREMENTS
Processor - I3/Intel Processor
Hard Disk - 160GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
RAM - 8GB
SOFTWARE REQUIREMENTS:
Operating System : Windows 7/8/10
Server side Script : HTML, CSS
Programming Language : Python
Libraries : Streamlit, Os, pandas, Scikit-learn, Numpy
IDE/Workbench : VsCode
Technology : Python 3.8+
Database : sqllite
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
