senior data scientist retail forecasting
creator sktime forecasting toolbox
- From Python forecasting basics to foundation models in four weeks
- Use-case driven with examples, methodologically solid
- Learn to code in sktime, the leading forecasting framework
- Onboard to the sktime community of practice - stay up-to-date forever!
- No biased product placement or hidden advertisement of paid services
- Become an expert and support open source - 50% towards sktime maintenance and development
A gentle introduction for newcomers, and a deep-dive into advanced topics such as hierarchical forecasting, foundation models
endogenous and exogenous data
multivariate, multi-instance
panel and hierarchical data
forecasting pipelines, feature extraction
parameter tuning, AutoML
ensemble models
prediction intervals and quantiles, distributional forecasts
probabilistic forecasting metrics
evaluation and benchmarking
hierarchical composite models, pooling
hierarchical reconciliation
global forecasting, cross-learning
deep learning, torch, pytorch-forecasting
foundation models, weight handling, pre-training & fine-tuning
Hugging Face and weight repositories
easily reusable and deployable code snippets
easy to extend for your private projects
join the community and stay up to date forever!
Senior Staff Data Scientist at ALDI Süd, forecasting for retail
Director, German Center of Open Source AI, and founder of sktime
Content creator tech & AI, founder and data science consultant and MLBoost
sktime core developer, forecasting for e-commerce, finance, and retail
Ideal for beginners with a grasp of machine learning. Learn time series forecasting in Python, from the ground up.
Expand and complete your toolkit, stay up-to-date. Learn about the newest techniques and cutting-edge methods.
For professional practitioners. Learn from basics to advanced techniques, tailored for real-world impact, with use-case examples.
Live sessions
Learn directly from your instructors in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Nov
24
Live Session with Robert | Introduction to the Course and Basic Forecasting with sktime
Nov
26
Live Session with Robert | Feature Engineering and Tuning
Dec
1
Live Session with Felipe | Panel and Hierarchical Forecasting
Dec
3
Live Session with Felipe | Deep Learning for Forecasting

Gentle introduction to foundation and pre-trained models. Zero-shot, few-shot, fine-tuning; open vs gated, etc
special considerations and "FMops" for forecasting - FM handling (best) practice, key difficulties & solutions
lock-in tactics, influencer propaganda, gated/closed models, licenses, full-value open alternatives
Live sessions
5 hrs / week
Mon, Nov 24
4:00 PM—5:30 PM (UTC)
Wed, Nov 26
4:00 PM—5:30 PM (UTC)
Mon, Dec 1
4:00 PM—6:00 PM (UTC)
Projects
2 hrs / week
Async content
1 hr / week
