PyData London 2016
This talk will present best-practices and most commonly used methods for dealing with irregular time series. Though we’d all like data to come at regular and reliable intervals, the reality is that most time series data doesn’t come this way. Fortunately, there is a long-standing theoretical framework for knowing what does and doesn’t make sense for corralling this irregular data.
Irregular time series and how to whip them
History of irregular time series
Statisticians have long grappled with what to do in the case of missing data, and missing data in a time series is a special, but very common, case of the general problem of missing data. Luckily, irregular time series offer more information and more promising techniques than simple guesswork and rules of thumb.
Your best options
I’ll discuss best-practices for irregular time series, emphasizing in particular early-stage decision making driven by data and the purpose of a particular analysis. I’ll also highlight best-Python practices and state of the art frameworks that correspond to statistical best practices.
In particular I’ll cover the following topics:
Visualizing irregular time series
Drawing inferences from patterns of missing data
Correlation techniques for irregular time series
Causal analysis for irregular time series
Slides available here: https://speakerdeck.com/aileenanielsen/irregular-time-series-and-how-to-whip-them