This week, we unveil the third issue in our ongoing series of tutorials on data preparation. This time, our focus is another bedrock assumption in time series modeling: homogeneity, or the assumption that a time series sample is drawn from a stable/homogeneous process.

We start by laying out a workable definition of a homogeneous stochastic process, then running through the minimum requirements for time series analysis. Then we’ll use sample data drawn from our previous tutorials to draw a few observations about homogeneity and examine the underlying assumptions behind them.

You can find more details and a step-by-step tutorial, along with a downloadable spreadsheet and PDF, at the link below:

Data preparation no. 3: Homogeneity

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