algo-forecast-prophetlisted

# Prophet Forecasting ## Overview Prophet (Meta) decomposes time series into trend + seasonality + holidays + error. Uses an additive (or multiplicative) model fitted with Stan. Handles missing data, outliers, and holiday effects natively. Designed for business time series at daily/weekly granularity. ## When to Use **Trigger conditions:** - Forecasting business metrics (sales, traffic, engagement) at daily/weekly frequency - Data with strong seasonal patterns and known holiday effects - Need quick, reasonable forecasts without deep time series expertise **When NOT to use:** - For high-frequency data (sub-hourly) — Prophet is designed for daily+ - When you need causal/explanatory models (Prophet is descriptive) - For very short time series (< 2 seasonal cycles) ## Algorithm ``` IRON LAW: Prophet Is an Additive Regression Model, NOT Classical Time Series y(t) = g(t) + s(t) + h(t) + ε(t) - g(t): piecewise linear or logistic trend with automatic changepoints - s(t): Fourier series for yearly/weekly/daily seasonality - h(t): user-specified holiday effects Prophet does NOT model autocorrelation in residuals. If residuals are autocorrelated, the uncertainty intervals will be too narrow. ``` ### Phase 1: Input Validation Prepare DataFrame with columns: ds (datestamp), y (metric). Add regressor columns if available. Specify: country holidays, custom holidays, growth type. **Gate:** Data formatted, minimum 2 full seasonal cycles. ### Phase 2: Core Algorithm 1. Choose growth m