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.
Questo esercizio fa parte del corso
Machine Learning for Time Series Data in Python
Istruzioni dell'esercizio
- Define a
percent_changefunction 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 tolast_value) - Calculate the percentage difference between the last value and the mean of earlier values.
- Extract all but the last value of the input series (assigned to
- Using a rolling window of 20, apply this function to
prices, and visualize it using the given code.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# 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()