This repo contains the code used to generate all of the plots in Clare and Alf's talk at Strata NYC in September 2019.

Time series forecasting is everywhere. It tells you what tomorrow’s temperature will be, your company’s stock price on Friday, and your blood glucose levels before bed. Often these forecasts depend on sensors or measurements made out in the real, messy world. Those sensors flake out, get turned off, disconnect, and otherwise conspire to cause missing data in your signals.

Alfred Whitehead and Clare Jeon explore a number of methods for handling data gaps and advise you on which to consider and when. You’ll see how to perform tests to determine which method suits your problem the best. And all of this is illustrated with real data from a continuous blood glucose monitor.

The methods they handle include random assignment, average-based imputation, last observed carried forward, linear interpolation, spline interpolation, moving average, Kalman smoothing with structural model, Kalman smoothing with auto-ARIMA model, Stineman interpolation, k-nearest neighbors, and seasonality with Prophet.

##Prerequisite knowledge A basic understanding of statistics, load, and how to manipulate data in R or Python with pandas

Understand the variety of methods available to impute missing data and a sense of how to apply them effectively

Alf Whitehead awhitehead@klick.com Clare Jeon cjeon@klick.com