<jupyter_start><jupyter_text>Time Series For data sources such as usage logs, sensor measurements, and financial instruments, the presence of a time-stamp results in an implicit temporal ordering on the observations.In these applications, it becomes important to be able to treat the time-stamp as an index around which we can perform several important operations such as:grouping the data with respect to various intervals of time* aggregating data across time intervals* transforming data into regular discrete intervals* windowing operations based on data from the previous time periodsIn this chapter, we will use a dataset obtained from the UCI machine learning repository. The dataset contains measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. The entire dataset contains around 2,075,259 measurements gathered between December 2006 and November 2010 (47 months).   IMPORTING DATA<jupyter_code>import graphlab as gl
import datetime as dt
household_data = gl.SFrame(
      "https://static.turi.com/datasets/household_electric_sample/household_electric_sample.sf")
household_data<jupyter_output><empty_output>
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