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
Stack Five Models for a Kaggle-Style Forecasting Bake-Off
You receive a pseudonymized dataset of 24 months of daily shipment volumes across about 200 origin-destination lanes plus weather and holiday features. Train 5 base models, use …
- Ensemble Methods
- Time Series Forecasting
- Feature Engineering
Advanced Machine Learning - CodeAdvancedNew
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
AI and Quantitative Finance - CodeAdvancedNew
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)
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.
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