Time Series Plots: Definition & Features

Instructor: Matthew Bergstresser

Matthew has a Master of Arts degree in Physics Education. He has taught high school chemistry and physics for 14 years.

Time series plots contain data with respect to time. In this lesson, we will analyze what a time series plot is and learn how they are used to analyze data.


Do you know the one thing that can't be stopped? It's time! Time marches on no matter what we do, and we analyze many things with respect to time. How many miles our cars go per hour, daily and monthly temperatures, and economic stocks are a few items that commonly incorporate time. Let's go through the characteristics of a time series plot and look at some examples of them.

Times Series Plots

A time series plot is a graph where some measure of time is the unit on the x-axis. In fact, we label the x-axis the time-axis. The y-axis is for the variable that is being measured. Data points are plotted and generally connected with straight lines, which allows for the analysis of the graph generated.

From the graph generated by the plotted points, we can see any trends in the data. A trend is a change that occurs in general direction. For example, if we see a car at a red light and then the light turns green, we could plot the distance the car moves versus the time it takes to get to its current position. We would notice the trend of an increasing distance from the starting point.

Distance versus time graph

We can see from the distance versus time graph that there are a couple of trends. From 0 to 6 seconds we can see the distance from the starting point is increasing. From 6 to 11 seconds the trend is that the object came to a stop and isn't moving because it remains at the distance of 10 meters for 5 seconds. Then, the object starts moving back to the location where it started.

Another type of time series plot is a bar graph. A bar graph uses horizontal or vertical rectangles to represent an amount. Data surrounding the cases of Lyme disease in the United States from 1992 - 2004 is represented by month on a time series plot.

Lyme disease cases over time is represented by a bar graph

We can see from the cases of Lyme disease graph there is a seasonality trend in the data. Seasonality is the predictable change in something based on the season. Lyme disease is an affliction that results from being bitten by a tick that is carrying a specific bacterium. Ticks thrive during the warm summer months, and outdoor activities are very common for people during the summer months. The graph shows there is an obvious correlation between warm weather and cases of Lyme disease.

A short-term fluctuation on a time series plot can be seen on a graph of temperature versus time. Measuring ocean temperatures over several decades shows a trend in ocean temperatures. It also shows short-term fluctuations.

Global ocean heat versus time

Looking at the global ocean heat versus time graph, we can see the trend shows that global ocean temperatures are increasing. There is one significant time range that deviates from this trend. Take a look at the graph from right after 1962 to around 1967. Global ocean temperatures decreased! Any other drop in global ocean temperatures was not nearly as significant as during the early to mid 60's. This is called an unsystematic change because this variation doesn't follow the general trend.

The data collected during this time could be considered an outlier, which is a data point or points that are obviously not in line with the rest of the data. The reason for outliers could be a change in equipment used to take the measurements, faulty equipment, a change in methodology for collecting data, or simply human error.

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