Transforming raw data

In the last chapter, you calculated the rolling mean. In this exercise, you will define a function that calculates the percent change of the latest data point from the mean of a window of previous data points. This function will help you calculate the percent change over a rolling window.

This is a more stable kind of time series that is often useful in machine learning.

This exercise is part of the course

Machine Learning for Time Series Data in Python

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Exercise instructions

Define a percent_change function that takes an input time series and does the following:
- Extract all but the last value of the input series (assigned to previous_values) and the only the last value of the timeseries ( assigned to last_value)
- Calculate the percentage difference between the last value and the mean of earlier values.
Using a rolling window of 20, apply this function to prices, and visualize it using the given code.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Your custom function
def percent_change(series):
    # Collect all *but* the last value of this window, then the final value
    previous_values = series[:____]
    last_value = series[-1]

    # Calculate the % difference between the last value and the mean of earlier values
    percent_change = (____ - np.mean(previous_values)) / np.mean(previous_values)
    return percent_change

# Apply your custom function and plot
prices_perc = prices.rolling(20).____
prices_perc.loc["2014":"2015"].plot()
plt.show()

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