What are the best ways to display time series data visually?

1 Choose the right chart type

The first step to display time series data visually is to choose the right chart type for your data and your message. The most common chart types for time series data are line charts, area charts, bar charts, and candlestick charts. Line charts and area charts are good for showing the overall trend and variation of a single or multiple variables over time. Bar charts are useful for comparing discrete values or categories over time. Candlestick charts are popular for showing the high, low, open, and close values of a variable over a period of time, such as a day, a week, or a month.

2 Use appropriate time intervals

The second step to display time series data visually is to use appropriate time intervals for your data and your message. Time intervals are the units of time that you use to group or aggregate your data, such as hours, days, weeks, months, or years. The choice of time intervals can affect how your data looks and how your audience interprets it. For example, if you use too small time intervals, your data may look noisy and chaotic. If you use too large time intervals, you may miss important details and patterns. You should use time intervals that match the frequency and seasonality of your data, as well as the level of detail and granularity that you want to convey.

3 Highlight key points and events

The third step to display time series data visually is to highlight key points and events that you want to draw attention to or explain. Key points and events are the values or periods of time that are significant, interesting, or unusual in your data, such as peaks, troughs, outliers, changes, or milestones. You can highlight key points and events by using colors, labels, annotations, markers, or icons. For example, you can use a different color to show a period of high or low performance, a label to indicate a specific date or value, an annotation to provide additional information or context, a marker to show a point of interest, or an icon to represent an event or action.

4 Add context and comparison

The fourth step to display time series data visually is to add context and comparison to your data to make it more meaningful and insightful. Context and comparison are the elements that help your audience understand the relevance and significance of your data, such as benchmarks, targets, averages, baselines, or forecasts. You can add context and comparison by using reference lines, bands, zones, or ranges. For example, you can use a reference line to show a goal or a threshold, a band to show a confidence interval or a tolerance level, a zone to show a desirable or undesirable range, or a range to show the minimum and maximum values.

5 Simplify and declutter

The fifth step to display time series data visually is to simplify and declutter your data to make it more clear and readable. Simplifying and decluttering your data means removing or reducing the elements that are unnecessary, redundant, or distracting, such as too many variables, too many colors, too many labels, or too much detail. You can simplify and declutter your data by using filters, aggregations, smoothing, or sampling. For example, you can use filters to show only the relevant or important variables, aggregations to show the summary or average values, smoothing to reduce the noise or variation, or sampling to show a representative or random subset of your data.

6 Test and refine

The sixth and final step to display time series data visually is to test and refine your data to make sure it is accurate, reliable, and understandable. Testing and refining your data means checking and correcting the errors, inconsistencies, or biases that may affect your data quality, validity, or credibility, such as missing values, outliers, incorrect scales, or misleading axes. You can test and refine your data by using validation, verification, or feedback. For example, you can use validation to ensure that your data meets the standards and specifications of your data source, verification to confirm that your data matches the reality and expectations of your data domain, or feedback to solicit and incorporate the opinions and suggestions of your audience or experts.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?