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 Questions? Comments? We’d love to hear your ideas for future issues or feedback on past issues. Contact us at [email protected].
