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>
Demand Forecasting Case Study
Privacy-safe demonstration of a demand-forecasting delivery flow using only synthetic data, written as an executive-readable AI delivery case study.
What it demonstrates
- Monthly demand by synthetic SKU and region.
- A baseline forecast: the mean of the preceding eight observed months after removing one minimum and one maximum.
- One gradient-boosting model using lag and calendar features.
- Leakage-safe rolling-origin validation for a 12-month horizon.
- A manual trigger boundary that can later be connected to Airflow.
- Annotated charts and written observations connecting modeling choices to delivery governance.
- An optional, gated build-vs-buy comparison against a pretrained TimesFM foundation model (
RUN_TIMESFM=1), evaluating operational cost alongside accuracy instead of switching models by default.
The notebook calculates evaluation metrics for model selection, but the published case-study narrative intentionally avoids numeric business claims.
The notebook is structured for an executive audience: it starts with the business question, explains why the baseline exists, visualizes the signal and validation trade-off, and closes with delivery implications for a lean AI task force.
Run
uv sync
uv run jupyter notebook notebooks/forecasting_case_study.ipynb
To execute the notebook non-interactively:
uv run jupyter execute notebooks/forecasting_case_study.ipynb --inplace
To also run the optional TimesFM foundation-model comparison (downloads ~500M-parameter weights
from Hugging Face; needs torch/transformers and internet access):
RUN_TIMESFM=1 uv run --with torch --with transformers jupyter execute notebooks/forecasting_case_study.ipynb --inplace
Privacy boundary
This repository contains no company rows, identifiers, schemas, credentials, private URLs, or copied business values. Review docs/publication-checklist.md before publishing.
Artifacts
notebooks/forecasting_case_study.ipynb— reproducible analysis.docs/architecture.mmd— editable architecture diagram.docs/architecture.svg— editable/exportable diagram asset used by the handout.docs/case-study-one-page.md— Markdown source for the interview handout.docs/case-study-one-page.html— print-ready handout source.docs/case-study-one-page.pdf— generated one-page interview handout.docs/linkedin-post.md— publication draft.