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 "is it a holiday?". 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.
This exercise is part of the course
Machine Learning for Time Series Data in Python
Exercise instructions
- Calculate the day of the week, week number in a year, and month number in a year.
- Add each one as a column to the
prices_percDataFrame, under the namesday_of_week,week_of_yearandmonth_of_year, respectively.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Extract date features from the data, add them as columns
prices_perc['day_of_week'] = prices_perc.____.____
prices_perc['week_of_year'] = prices_perc.____.____
prices_perc['month_of_year'] = prices_perc.____.____
# Print prices_perc
print(prices_perc)