Evidence memo

Forecast Studio

This memo clarifies what Forecast Studio demonstrates: forecasting as an operational system, not a notebook. It prioritises backtesting discipline, feature pipeline thinking, and deployment-ready outputs.

Time-Series Backtesting MAE / MAPE MLOps Patterns Monitoring-ready Outputs

Signal → Systems → Proof Ledger → Forecast Studio Memo

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CV anchor: /evidence/#forecast-studio

What this proves

Forecasting systems often fail in production because evaluation is weak, pipelines are not repeatable, and outputs are not operational. This build focuses on those failure modes.

Feature Pipelines Backtesting Harness Error Reporting Retraining Triggers Monitoring Mindset

Evaluation discipline: backtesting mindset rather than a single split score.
Operational pipeline thinking: repeatable transforms and deployment-shaped outputs.
Full-stack framing: modelling choices plus engineering constraints (latency/cost where applicable).
Production narrative: clear hand-off points for deployment, monitoring, and retraining decisions.

How to verify (60 seconds)

Step 1 — Open the system and run the demo

The demo is an in-browser illustration that shows horizon selection and baseline comparison logic. It is intentionally lightweight and reliable for public hosting.

Expected: horizon selection changes the forecast behaviour.
Expected: output shows a demo MAE computed on a simple split.

Step 2 — Inspect the repo structure

Verify that the project is organised as a system: data handling, modelling, evaluation, and artefacts.

Expected: separation of data transforms vs training vs evaluation.
Expected: conventions for outputs and reproducibility.

Key design choice

The portfolio demo is intentionally client-only. In production, the same structure maps to a scheduled job or service that writes forecasts to a store and exposes them via API for downstream planning.

Cloudflare Pages No Backend Calls CORS-safe System Thinking

Production risks & mitigations

Leakage & evaluation bias

Mitigation: backtesting mindset and explicit splitting.
Mitigation: consistent transforms across train/test logic.
Mitigation: prefer error reporting across horizons.

Drift & retraining

Mitigation: define monitoring signals (input drift, error drift).
Mitigation: establish retraining triggers tied to business impact.
Mitigation: version outputs for auditability.

Next improvements (production path)

Add a data validation layer (schema checks, missingness thresholds).
Implement scheduled runs + model registry + forecast store.
Add performance monitoring once realised outcomes are available.
Support multiple models with champion/challenger evaluation.

Keywords (ATS trigger set)

Time-Series Forecasting Backtesting MAE MAPE Feature Engineering MLOps Monitoring Retraining Triggers Deployment-ready Outputs

Proof anchor for CV: /evidence/#forecast-studio