The first half of the workshop will focus on common time series algorithms from a theoretical and applied perspective, including ARIMA/singular spectrum analysis/recurrent neural networks for prediction, topological/geometric approaches for change-point detection, and longitudinal statistical models for statistical testing of factors influencing time series values. The applications will involve Python code and datasets such as the Moroccan stock market, food prices in Burkina Faso, health indicators in Gabon, and climate change in Sudan, among others. Jupyter notebooks and datasets will be provided ahead of time for students to follow along during this half of the workshop, and we'll be making the materials from the talk available to anyone who isn't able to attend but would like to learn. The second half of the workshop will involve a Hackathon using either the Humanitarian Exchange and Zindi open-source datasets from our examples or whatever data participants want to bring. Focus will be on applications that solve problems and could be scaled into papers or businesses. Build teams. Work independently. Come up with a solution in the workshop, or build out a longer-term project! 

 Participants will come away with some theoretical knowledge of time series data and techniques to model time series data, as well as knowledge of how to implement these techniques in Python. It is hoped that the Hackathon part will equip participants with the skills to solve real-world problems in their home countries that involve time series data--tracking climate change to predict future impacts, understanding market and financial instability from events such as the COVID pandemic, testing healthcare interventions over time on reduction of mortality, predicting future food prices in local markets... These can be the start of research papers, government grants, or businesses, and we're happy to help participants build out ideas after the workshop or connect them with those who can help build out the ideas in their home countries. 

It's helpful if participants know some pandas and scikit-learn prior to participating. As long as they can run Python, it's okay to use a mobile phone or computer (or share with a Hackathon partner). If they'd like, they can download data and code prior to the conference. If not, we're happy to provide that upon the start of the session. For more advanced methods (deep learning, topological data analysis), it's helpful if participants have some familiarity with the math, but we're hoping to keep the technical explanations to the basics.