We will break down our discussion of exploratory data analysis for time series into two distinct sections. First, we discuss the application of commonly used data methods on time series. In particular, we discuss how histograms, plotting, and group-by operations can be applied to time series data.
Second, we highlight fundamentally temporal methods for time series analysis—that is, methods developed specifically for time series data and that make sense only in the context of data points that have a temporal relationship with one another (rather than a cross-sectional one).
We begin by thinking about how to apply commonly used data exploration techniques to time series data sets. The process is the same as what you have performed on non-time-series data to start with.
You will want to know the columns that are available, their value ranges, and what logical units of measurement work best. You will want to address the same exploratory questions you would ask about any new data set, such as:
Are any of the columns strongly correlated with one another?
What is the overall mean of an interesting variable? What is its variance?
To answer these, you can use familiar techniques such as plotting, taking summary statistics, applying histograms, and using targeted scatter plots. You’ll also want to answer explicitly time-oriented questions such as:
What is the range of values you see, and do they vary by time ...