Time series data is an important form of structured data in many different fields, such as finance, economics, ecology, neuroscience, and physics. Anything that is observed or measured at many points in time forms a time series. Many time series are fixed frequency, which is to say that data points occur at regular intervals according to some rule, such as every 15 seconds, every 5 minutes, or once per month. Time series can also be irregular without a fixed unit of time or offset between units. How you mark and refer to time series data depends on the application, and you may have one of the following:
Timestamps, specific instants in time
Fixed periods, such as the month January 2007 or the full year 2010
Intervals of time, indicated by a start and end timestamp. Periods can be thought of as special cases of intervals
Experiment or elapsed time; each timestamp is a measure of time relative to a particular start time (e.g., the diameter of a cookie baking each second since being placed in the oven)
In this chapter, I am mainly concerned with time series in the first three categories, though many of the techniques can be applied to experimental time series where the index may be an integer or floating-point number indicating elapsed time from the start of the experiment. The simplest and most widely used kind of time series are those indexed by timestamp.
pandas also supports indexes based on timedeltas, which can be a useful way of representing experiment or ...