Stock Market Prediction Model: Prophet, LSTM, Linear Regression with PyTorch
Overview
This project develops a sophisticated stock market prediction model by integrating three different approaches: Facebook's Prophet, LSTM (Long Short-Term Memory), and Linear Regression, implemented using PyTorch. The model aims to accurately forecast stock prices by analyzing historical data obtained from Yahoo Finance (yfinance).
Features
- Prophet: Captures underlying trends and seasonality in stock prices.
- LSTM Neural Networks: Learns complex temporal patterns in stock market data.
- Linear Regression: Combines insights from Prophet and LSTM to predict future stock prices.
- Data Source: Utilizes Yahoo Finance for reliable and up-to-date stock data.
Prerequisites
- Python 3.6+
- PyTorch
- Pandas
- FBProphet
- yfinance
- Matplotlib (for visualization)
