This is the repository for the "Introduction to Machine Learning with Time Series" workshops at the 2023 Congreso Internacional de Ingeniería de Sistemas at the Universidad de Lima and the 2023 INTERCON conference.
You will learn about:
- How to do exploratory data analysis for time series,
- How to identify different time series learning problems,
- How to build machine learning models to solve these problems.
There are various Python packages for machine learning with time series (see this overview). For this workshop, we're mainly using aeon, a fork of sktime.
The workshop assumes familiarity with the cross-sectional machine learning setting covered by scikit-learn, but no prior experience of working with time series.
You can run the notebooks on Binder without having to install anything.
Alternatively, you can clone this repository and run the notebooks locally. This requires a working Python installation (e.g. Anaconda distribution) with Jupyter notebooks. Additional requirements are in .binder/requirements.txt.
- Contribute to open source projects
- Google Summer of Code internships
- Outreachy internships
- Open Life Science mentoring and training program for building open-science communities, also see The Turing Way
- Major League Hacking internships
Feedback is highly appreciated. If you've found an error, if I've missed anything or if you want to suggest something new, please raise an issue.