A Tour of Time Series Analysis
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.
README.md Update README.md Aug 25, 2016
presentation_final.pdf date fixed! Aug 16, 2016


A Tour of Time Series Analysis

Talk will be given to PyData San Francisco on 13 August 2016. Summary:

  • Covers several time series methods, with a focus on score-driven models
  • Uses the PyFlux library to perform analysis with these models.
  • Contains a simple NFL prediction model - a starting point for those getting into sports analytics.


  • I said dropout approximates a 'Bayesian model': I meant to say it approximates Bayesian inference (the model itself isn't Bayesian or frequentist per say). See https://arxiv.org/abs/1506.02142.
  • I mispoke and said "metrics like KL divergence" - while KL looks like a mathematical metric, it doesn't actually satisfy the triangle inequality so isn't a metric in the strict sense; see the literature on Bregman divergences.
  • I often said overround instead of overflow in the Q&A; I used to be a sports quant so perhaps the former comes more naturally to my mind...