[MUSIC] So we keep talking about
this housing application. And it's really a nice intuitive way
to describe the different methods in regression that we're gonna
be talking about, and that we have been talking about. But regression, like we've mentioned, is
much, much, much more widely applicable. And this notation of
capturing seasonality also appears in lots of applications
beyond just housing. And so I wanted to spend a little time
talking about other places where we see this seasonality or
seasonal effects coming into play. And one, which makes a lot of sense,
is if you're doing weather modeling. Let's say you're trying to predict
the temperature or rainfall. Well, if you're thinking
about temperature, well there's variation in
temperature across a day. It's hotter during the daytime hours and
cooler during the night. But of course, it's also hotter in
the summer and cooler in the winter. So there's actually this seasonality
at different time scales. So in addition to just
having that one sine and cosine that we showed in that
housing model, where in that case, we're just just looking at this
monthly effect repeating every year. Well, for weather modelling,
if you're predicting temperature, you might wanna add in a sine and cosine functions at different frequencies
to capture the fact that there are these daily effects as well as these monthly
effects, and maybe other effects as well. Also you might be thinking about Flu
monitoring, so you wanna think about monitoring the incidence rate of flu and,
for example here, the picture I'm showing is in the United States in
a whole bunch of different regions. And if we look at any one of those regions
and we look at rate of flu over time, well of course there are gonna
be peaks during flu season and valleys during the off months. And so you see this kind of seasonal
pattern in flu monitoring and lots of other types of
health monitoring like this. And you'll also see it in the things
like E-commerce, for example, Amazon is really interested in being able to
stock their inventory pretty accurately. And if you're thinking about selling
jackets, you have some warehouse here in the US and you wanna figure out how many
snow jackets or ski jackets to stock. Well, of course, you're gonna wanna stock
more in the winter months than you would in the summer months because more
people are likely to purchase jackets in the winter than in the summer. So that's another place where
seasonality is really important. And it appears in so many applications. Another one that you might not
think of is Motion capture, just trying to model how
a person walks over time. And if you look at the data, if you
put sensors over a person's body and look at how they walk, if you take
those recordings, you're gonna get these kind of up and down, up and
down swings, as the person's going through their different motions, raising
their knees or walking or their arms. And so in this plot here, I'm looking at
some center trajectories from a person wearing a motion capture suit as they're
going through different behaviors, and you clearly see this type of
seasonality here as well. [MUSIC]