Hi, my name is Michael Schmidt. I'm the Chief Scientist at DataRobot.
Today I'll be talking about time series forecast. Time series modeling is one of
the hardest problems in data science because you're trying to take recent
history and predict what's going to happen next. So let's look at an example of time
series data set. Time series data sets mean that you're tracking multiple
signals over time. So a simple example might be just tracking the sales of a
product or the sales for a store. And you might have data that looks like this
that you're tracking up to a certain point. Let's call this "now" for example. So
based upon this recent history what you'd like to do is use that to
train a model that tells you what will happen next. So we'd like to be able to
extend this line and say this is what we think the sales are going to be. Are they
may go up, are they moved down, or they're going to flatten out.
So there are endless use cases where this can be extremely valuable to know and it's a
really hard problem to do because you need to look back at this history and
make the most accurate guess or prediction of what happens next. So one
of the reasons why time series modeling so hard and so difficult is there's
many different types of time series data sets and that was just one of them. So
let me show you a couple of others. She might have some that look like this,
where there's a trend with some periodic variation. Right. So another example might
be one that has no periodic variation. So you might have one that looks
this right, but it has some like growth over time. A third type you might be
interested in is something like sensor data. Where it's a stationary signal but
it has some variation going on and you'd like to be able to anticipate what happens
on this type of data set as well. So given these different flavors of
problems, you typically treat these very differently.
You would take different approaches. You'd build different types of models.
And what we need to be able to do is build a system that can identify what's
the right approach to take, what's the right algorithm, what are the right
features engineer in order to give you the highest possible predictive accuracy
for these problems.
