Exercise

Non-seasonal differencing for stationarity

Differencing is a way of making a time series stationary; this means that you remove any systematic patterns such as trend and seasonality from the data. A white noise series is considered a special case of a stationary time series.

With non-seasonal data, you use lag-1 differences to model changes between observations rather than the observations directly. You have done this before by using the diff() function.

In this exercise, you will use the pre-loaded wmurders data, which contains the annual female murder rate in the US from 1950-2004.

Instructions

100 XP
  • Plot the wmurders data and observe how it has changed over time.
  • Now, plot the annual changes in the murder rate using the function mentioned above and observe that these are much more stable.
  • Finally, plot the ACF of the changes in murder rate using a function that you learned in the first chapter.