Deep-Learning-with-PyTorch

Working with time series
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network from scratch. We will also revisit how to better grade our results in chapter 12.
Let’s move on to other data types for now.
4.4 Working with time series
In the previous section, we covered how to represent data organized in a flat table. As
we noted, every row in the table was independent from the others; their order did not
matter. Or, equivalently, there was no column that encoded information about what
rows came earlier and what came later.
Going back to the wine dataset, we could have had a “year” column that allowed us
to look at how wine quality evolved year after year. Unfortunately, we don’t have such
data at hand, but we’re working hard on manually collecting the data samples, bottle
by bottle. (Stuff for our second edition.) In the meantime, we’ll switch to another
interesting dataset: data from a Washington, D.C., bike-sharing system reporting the
hourly count of rental bikes in 2011–2012 in the Capital Bikeshare system, along with
weather and seasonal information (available here: http://mng.bz/jgOx). Our goal
will be to take a flat, 2D dataset and transform it into a 3D one, as shown in figure 4.5.
time of day
weather
temperature
humidity
wind spEed
bike count
etc.
day 1 day 2 day 3
<-midnight - no0n - midnight->
<-midnight - no0n - midnight->
<-midnight - no0n - midnight->
weather
temperature
humidity
wind spEed
bike count
etc.
day 1
day 2
day 3
<-midnight - no0n - midnight->
weather
temperature
humidity
wind spEed
bike count
etc.
Figure 4.5 Transforming a 1D, multichannel dataset into a 2D, multichannel dataset by separating the date and
hour of each sample into separate axes