This is the repository for the "Introduction to Machine Learning with Time Series" tutorial in Python during Data Science for Social Good at The Alan Turing Institute (DSSGx) 2020.
You'll learn about:
- How to tell apart different learning time series learning problems (or tasks) that arise in a temporal data setting,
- How to do exploratory data analysis for time series,
- How to build machine learning models to solve these tasks (using sktime and other Python toolboxes),
We assume familiarity with the standard tabular 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 <https://help.github .com/en/github/creating-cloning-and-archiving-repositories/cloning-a -repository> this repository and run the notebooks locally. This requires a working Python installation (e.g. Anaconda distribution) with Jupyter notebooks.
Feedback is highly appreciated. If you've found an error, if we've missed anything or if you want to suggest something new, please raise an issue <https://github.com/mloning/intro-to-ml-with-time-series-DSSGx-2020/issues/new /choose>.