### What is a Time Plot?

A time plot, also known as a time series chart, displays values against time. These plots are similar to Cartesian graphs, but unlike a xy graph, which can display a wide range of “x” variables (for example, height and weight), time plots only allow time to be displayed on the x-axis. These plots are not like pie charts or bar charts. These plots can be used to show how data changes with time. This type of chart is useful if, for example, you are sampling data at random intervals.

### Understanding time-series data

Time series is a collection of data that are collected in regular intervals. **time series data** has a sequential nature, which is one of its most important features. Each data point depends on the previous one. This dependency can be visualized using a plot of data points in a sequence.

These two examples — **website data and stock prices over a period of time** — are time-series use cases. We can analyze website traffic using tools like Google Analytics to identify trends over time. This data can be visualized using a time plot, with the number of visitors to a website on the y-axis.

We can also collect stock prices at regular intervals, such as daily, hourly, or even every minute, to determine trends over time. This data can be visualized using a time plot, with the y-axis representing stock prices and time as the x-axis.

**Temperature readings over time** would also be a great example. Temperature readings can be taken at regular intervals in order to detect patterns of temperature change over time. This data can be visualized using a time plot, with temperature on the y-axis.

## Time-Series Plots: Variable Types

It is important to take into account the type of variable when plotting a chart. Time-series data is generally divided into quantitative and qualitative variables.

**Quantitative Variables** are numerical variables that can be quantified/measured in discrete or continuous form. Quantitative variables include, for example, stock prices and website traffic. These variables are usually plotted along the y-axis on a time series chart.

**Qualitative Variables** have non-numerical or categorical values. Qualitative variables include, for example, the name of a website, the type or product sold, and the location where an organization operates. They are usually not plotted in a time series chart because they don’t have a numerical value which can be represented by a continuous scale. We can still use them for data analysis.

## How to interpret a time-series plot?

You must be able to interpret a plot of time series by understanding the patterns of data over time. When interpreting a plot of a time series, you should consider the following factors:

**Seasonality**is a term that refers to patterns/cycles which repeat over a certain period. These patterns can be either weekly, monthly, or quarterly. Winter coat sales, for example, usually follow a seasonal pattern. They increase in the fall and the winter and then decrease in the spring.**Trend**: This refers to the direction in which the data is moving over time. A trend may be upward or downward, or it can remain constant. Positive trends indicate that values increase over time. Negative trends show values decreasing over time. A horizontal trend indicates that values are constant over time.**Outliers**: Values that are outside of the normal pattern of data. Outliers can be caused by random events or external factors. They can have a significant impact on the interpretation of a time-series plot.**Level**: This is the average value of the data over the whole time period. A higher level means that the values in general are higher and vice versa.