So, the ubiquity of time series data

can be shown simply by just looking at the newspaper.

If I take a random selection of 4,000 graphs from

50 newspapers and magazines worldwide from 1974 to 1980,

75 percent of these graphs were time series.

So, what's great about time series data is

that general audiences are familiar with time series.

They understand, they sort of have

been inundated with these graphs so they're used to looking at time series graphs,

where essentially we may have some time on the x-axis and value on y.

So this might be our stock market trend for example, right?

So looking at how the stock market changes over time.

So we can start thinking about what questions we want to ask about time series data,

not necessarily the questions of the visualization,

but if I'm giving you a time series data set,

what things do you want to know?

So some questions might be,

does a data object exist at a certain time?

So, for example, is there any robberies in my neighborhood at night?

Or, when does a certain object exist in the data?

If there are robberies,

what time of day do they occur?

How long does a data object exist?

So, for example, if we're tracking people at

an amusement park trying to think about how long they're waiting for in line for a ride,

how long is that person at that location waiting to get on the ride?

How fast and how much does that object change?

So think about trajectories and cars.

What order did the objects appear in?

Is there a cyclical pattern?

Which objects exist at the same time?

So, all of these are questions we can ask about our time series data,

and we want to start thinking about how we can explore and ask these questions.

Time has a lot of different elements to it.

Time can be and is ordered.

We think about time in a progression,

so we have yesterday, today, and tomorrow.

We split this up and think about what occurred before,

what occurred after, and we try to forecast what might come next.

In that sense, it's continuous.

But we also need to think about time as cyclical.

So we have hours of the days,

days of the weeks,

months of the year, seasons, and those sorts of things.

So things might repeat based on these underlying cyclical patterns of the environment.

Time can also be independent of location as well.

So, we can think about linear time versus cyclical time.

In linear time, one point precedes another,

and time being ordered is closely bound to the notion of causality.

So, we're going to talk in future modules

about space and how to explore data over space and time.

In this model, we want to focus primarily on time,

where one event happens and then another and so forth.

An event could just be a measurement in the stock market.

How much money did the stock market make this second,

then the next second, then the next.

It could be a measure of how much did temperature change

over time or what was the average temperature this year to the next.

Cyclical time is the ordering of points.