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Exercise

Other tranforms

Differencing should be the first transform you try to make a time series stationary. But sometimes it isn't the best option.

A classic way of transforming stock time series is the log-return of the series. This is calculated as follows: $$log\_return ( y_t ) = log \left( \frac{y_t}{y_{t-1}} \right)$$

The Amazon stock time series has already been loaded for you as amazon. You can calculate the log-return of this DataFrame by substituting:

  • \(y_t \rightarrow\) amazon
  • \(y_{t-1} \rightarrow\) amazon.shift(1)
  • \(log() \rightarrow\) np.log()

In this exercise you will compare the log-return transform and the first order difference of the Amazon stock time series to find which is better for making the time series stationary.

Instructions 1/2

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  • Calculate the first difference of the time series amazon to test for stationarity and drop the NaNs.