🔮 Time-Series Forecasting with Facebook Prophet

Leveraging additive regression models to predict future trends with seasonality handling.

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📖 Overview

Traditional forecasting models often struggle with missing data and trend shifts. This project utilizes Meta's Prophet library to handle time-series data with strong seasonal effects (daily, weekly, yearly).

🎯 Project Highlights

• Seasonality: Modeled complex holiday effects and weekly cycles.
• Robustness: Handled outliers and missing timestamps effectively.
• Prediction: Generated a 365-day future forecast with confidence intervals.

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🛠️ Architecture

• Library: fbprophet
• Data Processing: Pandas Time-Series
• Visualization: Plotly (Interactive forecasting plots)

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