Time Series Analysis and Forecasting Project Objective: This project explores quantitative forecasting methods to predict product sales using time series analysis. The notebook documents various statistical models and evaluation metrics to provide a comprehensive guide for sales forecasting.
Project Goal Apply multiple forecasting techniques—including time series models and causal models—to predict product sales using a Kaggle dataset. The analysis compares model performance and interprets key metrics for practical business insights.
Models Implemented Seasonal Naive Model (Baseline for seasonal patterns)
Holt-Winters Model (Exponential smoothing with trend/seasonality)
Linear Regression Model (Causal forecasting with external factors)
Key Features
✔ Detailed metric explanations (MAE, RMSE, MAPE)
✔ Visualizations of forecasts vs. actuals
✔ Interpretation of model strengths/weaknesses
Tools Used Python (pandas, statsmodels, scikit-learn)