930534dde2
Synthetic monthly SKU/region demand notebook comparing a trimmed-mean baseline against gradient boosting, with a gated TimesFM build-vs-buy comparison. Includes rolling-origin validation, executive narrative, architecture diagram, one-page PDF, and LinkedIn draft. No company data, credentials, or private implementation details. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
1.1 KiB
1.1 KiB
From demand question to forecast evidence
The case
Build a repeatable forecasting flow without exposing operational data. The public version uses synthetic monthly demand by SKU and region while preserving the delivery decisions that matter: data preparation, a transparent baseline, model comparison, validation, and a clear operating boundary.
Architecture
flowchart LR
source[Synthetic demand] --> prep[Preparation/features]
prep --> baseline[Trimmed-mean baseline]
prep --> model[Gradient boosting]
baseline --> validate[Rolling validation]
model --> validate
validate --> forecast[12-month forecast]
Delivery narrative
Start with a baseline that stakeholders can audit. Add one model only when validation shows a useful improvement. Keep the trigger manual while the workflow is being proved, then expose the same boundary to an orchestrator such as Airflow.
Evidence
The notebook computes validation metrics and produces comparison charts. This handout deliberately makes no numeric business claims because the data is synthetic.