Time Series Forecasting
If you like applying Time Series Forecasting, every challenge here gives you a chance to practice it on a real industry brief.
- CodeIntermediateNew
Forecast Energy Demand for a Nordic Renewable Utility
You receive 5 years of hourly residential-segment demand, hourly weather data (temperature, wind, irradiance), and a calendar of public holidays. Build a probabilistic forecaste…
- Time Series Forecasting
- Probabilistic Modeling
- Feature Engineering
Applied Machine Learning - CodeIntermediateNew
Forecasting Model for Online-Game Daily Active Users
Build forecasts at 14-day horizon per region using: (1) classical baseline — SARIMA or Prophet; (2) ML approach — gradient-boosted regressor on engineered features (day-of-week,…
- Supervised Learning
- Time Series Forecasting
- Python Programming
Machine Learning (CS Elective) - CodeIntermediateNew
Forecast Intraday FX Volatility for a London Liquidity Desk
You receive 18 months of tick-level mid-quote data for six FX pairs plus a calendar of scheduled macro events. Resample to 1-minute bars, engineer realized-volatility features, …
- Time Series Forecasting
- Feature Engineering
- Model Validation
Open coursework
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
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Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.