Demand history and forecast
Actual, outlier-adjusted history, EMA smoothing, and forward forecast with confidence bands.
Series
Python-based statistical forecasting with a simple static dashboard. Sample data stays local, forecasts are prebuilt into JSON, and the front end remains lightweight and easy to host.
Actual, outlier-adjusted history, EMA smoothing, and forward forecast with confidence bands.
Compare the next horizon across the three component models and the ensemble.
Static ensemble weights plus holdout RMSE from the Python modeling step.
Rolling z-score anomalies replaced with the local rolling median before modeling.
| Date | Type | Raw | Adjusted | Z-score |
|---|