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>
26 lines
1.1 KiB
Markdown
26 lines
1.1 KiB
Markdown
# From demand question to forecast evidence
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## The case
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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.
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## Architecture
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```mermaid
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flowchart LR
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source[Synthetic demand] --> prep[Preparation/features]
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prep --> baseline[Trimmed-mean baseline]
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prep --> model[Gradient boosting]
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baseline --> validate[Rolling validation]
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model --> validate
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validate --> forecast[12-month forecast]
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```
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## Delivery narrative
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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.
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## Evidence
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The notebook computes validation metrics and produces comparison charts. This handout deliberately makes no numeric business claims because the data is synthetic.
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