Apr-May 2025

A gentle introduction for newcomers, and a deep-dive into advanced topics such as hierarchical forecasting, foundation models

senior data scientist retail forecasting

Robert Kübler, Ph.D.

Senior Staff Data Scientist at ALDI Süd, forecasting for retail

creator sktime forecasting toolbox

Franz Király, Ph.D.

Director, German Center of Open Source AI, and founder of sktime

content creator, founder MLboost

Mahdi Torabi Rad, Ph.D.

Content creator tech & AI, founder and data science consultant and MLBoost

forecasting for e-commerce and retail

Felipe Angelim, MSc

sktime core developer, forecasting for e-commerce, finance, and retail

Python Forecasting Masterclass: From Foundations to State-of-the-Art

sktime

- 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

Master forecasting in Python and join a growing community of practice

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.

Who is this course for

What you’ll get out of this course

<ul><li>terms, basic formalism</li><li>code representations</li><li>mathematical foundations</li><li>exploratory data analysis</li><li>pre-modelling checks</li></ul>

Time series basics

<ul><li>endogenous and exogenous data</li><li>multivariate, multi-instance</li><li>panel and hierarchical data</li><li>batch and stream data</li><li>diagnose your use case</li></ul>

Diagnose data and learning task accurately

<ul><li>statistical, ML, deep learning, AI &amp; foundation models</li><li>when to use what</li><li>build/adopt/extend trade-offs</li><li>model assurance, fair evaluation</li><li>benchmarking models</li></ul>

Overview of model types and tasks, best practice advice

<ul><li>forecasting pipelines</li><li>feature extraction, feature engineering</li><li>parameter tuning</li><li>ensemble models</li><li>AutoML</li></ul>

Model composition, pipelines, AutoML

<ul><li>prediction intervals and quantiles</li><li>distributional predictions</li><li>probabilistic forecasting metrics</li><li>evaluation and benchmarking</li></ul>

Probabilistic forecasting

<ul><li>hierarchical data</li><li>composite models</li><li>reconciliation</li><li>pooling for multilevel data</li><li>global forecasting, cross-learning</li></ul>

Hierarchical forecasting, global forecasting

<ul><li>deep learning</li><li>torch, pytorch-forecasting</li><li>foundation models</li><li>Hugging Face and weight repositories</li><li>weight handling, fine-tuning</li></ul>

Cutting edge model types and techniques

<ul><li>easily reusable and deployable code snippets</li><li>easy to extend for your private projects</li><li>join the community and stay up to date forever!</li></ul>

Using sktime - the leading forecasting package

Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

Meet your instructor

Cohort 25A: Apr 15 - May 8, 2025

Cohort 25B: June 3 - 26, 2025

Cohort 25C: Sep 1 - Sep 25, 2025

Course schedule

This course builds on live workshops and hands-on projects

Active hands-on learning

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

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About this course

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