Forecast Daily Demand for an Apparel Supply-Chain Team
Overview
What this challenge is about.
You forecast 14-day demand for 500 SKUs across 200 stores using SARIMA, LightGBM, and a TFT model. Get a verifiable certificate.
The Brief
What you'll do, and what you'll demonstrate.
Pick the demand-forecasting approach that gives the best MASE improvement over the SARIMA baseline at acceptable training cost.
Earning criteria — what you'll demonstrate
- Apply SARIMA, tree-based, and neural forecasters to real retail data
- Evaluate forecasts with rolling-origin folds (not random split)
- Quantify bias and error by forecast horizon
- Communicate a forecasting recommendation to a supply-chain audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Time Series Analysis and Forecasting
Master · Ai Ml
Fit score: 1
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career paths this builds toward
Canonical rolesData Scientist
Forecasting at SKU-store scale with rigorous rolling evaluation is the textbook senior data-scientist project at any large retailer.
This challenge sharpens
- time-series-forecasting
- rolling-evaluation
- sarima
Machine Learning Engineer
Comparing classical, tree-based, and neural forecasters across cost and accuracy is the same trade-off MLEs make for production deployment.
This challenge sharpens
- gradient-boosting
- neural-forecasting
- rolling-evaluation
Applied AI Scientist
Translating forecast error analysis into a supply-chain memo is the bread and butter of applied-AI work in operations-heavy retail.
This challenge sharpens
- time-series-forecasting
- neural-forecasting
- sarima