📈 Stock Price Forecasting using Facebook Prophet (Time Series Analysis)

This project focuses on time series forecasting of stock prices using Facebook Prophet, a robust and interpretable model developed by Meta. The aim is to predict future stock closing prices based on historical data without building traditional machine learning models.

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📁 Dataset

The dataset contains historical stock prices with these key features:

• Date
• Open
• High
• Low
• Close
• Volume

Data was reformatted for Prophet as:

• ds → Date
• y → Closing Price

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📝 Topics Covered

• Time Series Forecasting
• Facebook Prophet Model
• Data Preprocessing & Formatting
• Handling Time Gaps and Missing Values
• Trend and Seasonality Detection
• Forecasting Future Stock Prices
• Visualization of Forecast and Components
• Business Interpretation of Forecast Trends

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⚙️ Technologies Used

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Facebook Prophet (prophet)
• Jupyter Notebook

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🔍 Project Highlights

• Applied Facebook Prophet to perform time series forecasting on stock closing prices.
• Visualized historical trends and future projections.
• Interpreted model components including trend, weekly, and yearly seasonality.
• Extended the forecast into future periods with confidence intervals.

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🎓 What I Learned

• How to convert financial data into a Prophet-compatible time series format.
• Forecasting using additive models with automated trend/seasonality detection.
• Visual interpretation of Prophet outputs and confidence bounds.
• Simpler, scalable alternative to traditional machine learning for time-based data.

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