Using "date" information
It's easy to think of timestamps as pure numbers, but don't forget they generally correspond to things that happen in the real world. That means there's often extra information encoded in the data such as "is it a weekday?" or "what quarter is it?". This information is often useful in predicting timeseries data.
In this exercise, you'll extract these date/time based features. A single time series has been loaded in a variable called prices.
Questo esercizio fa parte del corso
Machine Learning for Time Series Data in Python
Istruzioni dell'esercizio
- Calculate the day of the week, month of the year, and quarter of the year.
- Add each one as a column to the
prices_percDataFrame, under the namesday_of_week,month_of_yearandquarter_of_year, respectively.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Extract date features from the data, add them as columns
prices_perc['day_of_week'] = prices_perc.____.____
prices_perc['month_of_year'] = prices_perc.____.____
prices_perc['quarter_of_year'] = prices_perc.____.____
# Print prices_perc
print(prices_perc)