Python Forecasting Masterclass: From Foundations to State-of-the-Art
4 Weeks
·Cohort-based Course
A gentle introduction for newcomers, and a deep-dive into advanced topics such as hierarchical forecasting, foundation models
Python Forecasting Masterclass: From Foundations to State-of-the-Art
4 Weeks
·Cohort-based Course
A gentle introduction for newcomers, and a deep-dive into advanced topics such as hierarchical forecasting, foundation models
Course overview
Master forecasting in Python and join a growing community of practice
- 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
Who is this course for
01
Ideal for beginners with a grasp of machine learning. Learn time series forecasting in Python, from the ground up.
02
Expand and complete your toolkit, stay up-to-date. Learn about the newest techniques and cutting-edge methods.
03
For professional practitioners. Learn from basics to advanced techniques, tailored for real-world impact, with use-case examples.
What you’ll get out of this course
Time series basics
- terms, basic formalism
- code representations
- mathematical foundations
- exploratory data analysis
- pre-modelling checks
Diagnose data and learning task accurately
- endogenous and exogenous data
- multivariate, multi-instance
- panel and hierarchical data
- batch and stream data
- diagnose your use case
Overview of model types and tasks, best practice advice
- statistical, ML, deep learning, AI & foundation models
- when to use what
- build/adopt/extend trade-offs
- model assurance, fair evaluation
- benchmarking models
Model composition, pipelines, AutoML
- forecasting pipelines
- feature extraction, feature engineering
- parameter tuning
- ensemble models
- AutoML
Probabilistic forecasting
- prediction intervals and quantiles
- distributional predictions
- probabilistic forecasting metrics
- evaluation and benchmarking
Hierarchical forecasting, global forecasting
- hierarchical data
- composite models
- reconciliation
- pooling for multilevel data
- global forecasting, cross-learning
Cutting edge model types and techniques
- deep learning
- torch, pytorch-forecasting
- foundation models
- Hugging Face and weight repositories
- weight handling, fine-tuning
Using sktime - the leading forecasting package
- easily reusable and deployable code snippets
- easy to extend for your private projects
- join the community and stay up to date forever!
This course includes
9 interactive live sessions
Lifetime access to course materials
In-depth lessons
Direct access to instructor
Projects to apply learnings
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
Python Forecasting Masterclass: From Foundations to State-of-the-Art
Course syllabus
Week 1
Apr
15
Live Session with Robert | Introduction to the Course and Basic Forecasting with sktime
Tue 4/153:00 PM—4:30 PM (UTC)Apr
17
Live Session with Robert | Feature Engineering and Tuning
Thu 4/173:00 PM—4:30 PM (UTC)
Introduction to the Course and Basic Forecasting with sktime
Feature Engineering and Tuning
Week 2
Apr
22
Live Session with Franz | Introduction to Probabilistic Forecasting
Tue 4/223:00 PM—4:30 PM (UTC)Apr
24
Live Session with Franz | Advanced Probabilistic Forecasting
Thu 4/243:00 PM—4:30 PM (UTC)
Introduction to Probabilistic Forecasting
Advanced Probabilistic Forecasting
Week 3
Apr
29
Live Session with Felipe | Panel and Hierarchical Forecasting
Tue 4/293:00 PM—4:30 PM (UTC)May
1
Live Session with Felipe | Deep Leaerning for Forecasting
Thu 5/13:00 PM—4:30 PM (UTC)
Panel and Hierarchical Forecasting
Deep Learning for Forecasting
Week 4
May
6
Live Session with Mahdi | Spatiotemporal Forecasting and Graph neural Networks
Tue 5/63:00 PM—4:30 PM (UTC)May
8
Live Session with Franz | Foundation Models for Forecasting
Thu 5/83:00 PM—4:00 PM (UTC)May
8
Live Session with Felipe | Writing your own Forecasters and Contributing to sktime
Thu 5/84:00 PM—5:00 PM (UTC)
Spatiotemporal Forecasting and Graph Neural Networks
Foundation Models for Forecasting
Writing your own Forecasters and Contributing to sktime
Meet your instructor
Franz Király, Ph.D.
Director, German Center of Open Source AI, and founder of sktime
Franz Király is director of the German Center for Open Source AI. He has 9 years experience as university professor at top tier universities, and 10 years of experience in industrial data science and AI roles, including 5 years at the Alan Turing institute - all of which included mentoring and teaching responsibilities. Dr Király is also the original founder and a core developer of sktime, the leading package for forecasting and AI with time series.
Robert Kübler, Ph.D.
Senior Staff Data Scientist at ALDI Süd, forecasting for retail
Robert Kübler is a Senior Staff Data Scientist at ALDI Süd and a passionate advocate for machine learning, algorithms, and data-driven problem-solving. He studied Mathematics and completed a Ph.D. in Cryptanalysis.
As a scientific author with over 70 articles on Towards Data Science and 1M+ views, Dr Kübler has a proven track record of making complex topics accessible to diverse audiences.
He has years of experience mentoring data scientists and leading real-world projects—from recommender systems to marketing mix modeling.
Felipe Angelim, MSc
sktime core developer, forecasting for e-commerce, finance, and retail
Felipe Angelim is a Tech Lead at Mercado Libre, the largest e-commerce platform in Latin America, a core developer at sktime, and the creator of Prophetverse. With over five years of experience in forecasting for finance and retail, he brings deep expertise in applying advanced techniques to real-world problems. By combining mathematical expertise with programming skills, he develops simple and effective solutions to complex challenges. Felipe studied Engineering at École Centrale de Lyon, France.
Mahdi Torabi Rad, Ph.D.
Content creator tech & AI, founder and data science consultant and MLBoost
Mahdi Torabi Rad is a data scientist, engineer, mentor, and YouTube content creator with over 10 years of experience in developing machine-learning models. Dr Torabi Rad creates videos for his YouTube channel MLBoost to share his knowledge, insights, and passion for AI with a wider audience, with a focus on forecasting, optimization, and modelling of complex physical systems.
Join an upcoming cohort
Python Forecasting Masterclass: From Foundations to State-of-the-Art
Apr-May 2025
$890
Dates
Payment Deadline
Course schedule
4-6 hours per week
Apr 15 - May 8, 2025
Tue & Thu, 15:00-16:30 UTC
Frequently Asked Questions
Join an upcoming cohort
Python Forecasting Masterclass: From Foundations to State-of-the-Art
Apr-May 2025
$890
Dates
Payment Deadline