STL decomposition notebook with parameter analysis, diagnostics, seasonal strength metrics, forecasting, and alternative methods. Complete time series decomposition workflow with visualizations and statistical tests.

Built and validated a linear regression model to predict annual customer spend using session and membership features; achieved R² = 0.981 and RMSE ≈ $10 on hold-out test data.

Project work for Time Series Analysis. Includes exploratory analysis, ARIMA modeling, diagnostics, forecasting, and evaluation using R. Covers trend/seasonality modeling, stationarity checks, ACF/PACF analysis, model selection, and forecast accuracy assessment.