Overview
Most time-series forecasting fails at method selection. Teams reach for Prophet because it is easy, for ARIMA because it feels rigorous, or for deep learning because it feels modern — without diagnosing whether the series actually justifies the method. The result is over-engineered forecasts that underperform simple exponential smoothing, or simple forecasts that miss clear seasonality.
The Time-Series Forecasting Framework provides a diagnostic-first approach: test stationarity, detect seasonality, identify trend, check for structural breaks — then select method based on series properties, validate via walk-forward backtesting, and produce prediction intervals that honestly reflect uncertainty.
What you get: - Stationarity testing (ADF, KPSS) - Seasonality detection (STL decomposition, ACF analysis) - Trend identification (Mann-Kendall, piecewise) - Structural break detection (CUSUM, Chow test) - Method selection decision tree (ETS / ARIMA / Prophet / ML) - Walk-forward backtesting protocol - Prediction intervals (not just point forecasts) - Residual diagnostics and model adequacy checks
Built for: data scientists, analysts, and forecasting practitioners who need a principled framework for choosing the right forecasting method per series — not a reflexive "Prophet for everything" approach.