This repository is for a hands-on presentation focused on using prophet for time series analysis. It covers:
- a high-level overview of time series analysis
- an illustration of Facebook's python package called prophet by using AirPassengers data, modeling the number of passengers flying to Austrailia in the 1960s.
- an application of prophet in the public sector (predicting rat activity in NYC neighborhoods)
For more information about this work and a step-by-step guide, see the ipynb file prophet_session.ipynb. Thanks!
If you'd like to learn more about my approach or have questions on the methods, please feel free to reach out to me directly at claire.k.boyd@gmail.com.
Implementation notes:
If you want to convert / update the .ipynb into .slides.html, you can do so using the jupyter nbconvert module, issuing the following command within your project directory:
jupyter nbconvert prophet_session.ipynb --to slides --post serve