PyData London 2016

This talk will present bestpractices 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 longstanding 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.

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I'll discuss bestpractices for irregular time series, emphasizing in particular earlystage decision making driven by data and the purpose of a particular analysis. I'll also highlight bestPython 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

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