Time series chart: definition, examples, and when to use one.

What is a time series chart?

A time series chart always puts time on one axis and a measured variable on the other. That is the one fixed rule that separates it from a general line chart, which can plot any two variables against each other, time-based or not. Because the intervals are evenly spaced and sequential, a time series chart is built to reveal three specific patterns: a trend over the long run, seasonality that repeats on a cycle, and anomalies that stand out from both.

Most time series charts are drawn as lines, though bars and areas are used too, depending on whether the story is about the trend, the total, or both.

A worked example

A finance team tracking monthly recurring revenue over a year would plot each month sequentially, with no gaps and no reordering.

Plotted month by month with equal spacing, this reads as a steady upward trend, with any month that breaks the pattern standing out immediately.

When a time series chart helps

• Financial reporting, tracking revenue, costs or margin month by month or quarter by quarter.
• Operational monitoring, watching daily order volume or response times closely enough to catch anomalies early.
• Marketing performance, plotting traffic, conversion or spend over a campaign period to judge its effectiveness.
• Forecasting, overlaying historical actuals with projected values to check whether the current path matches a target.

Time series chart vs line chart

Every time series chart is a line chart, but not every line chart is a time series chart. A line chart can plot any two continuous variables, price against demand, for instance, with no time involved at all. A time series chart specifically fixes one axis to time and carries the extra considerations that come with it: consistent intervals, seasonal cycles, and the risk of reading noise as trend.

Best practices

• Keep intervals consistent. Mixing monthly data with quarterly data on the same axis distorts the visual slope.
• Mark significant events directly on the chart, a pricing change or a launch, so a spike or a dip has context.
• Add reference lines for targets or a prior year, so the reader has a benchmark rather than judging the trend in isolation.
• Aggregate long horizons. Five years of daily data has too many points to read; roll it up to weekly or monthly first.

In Chartbuddy, build time series views with a line chart or an area chart.