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docs: time series add why use it and how to data needs (#92158)
Co-authored-by: Isabel Matwawana <isabel.matwawana@grafana.com> Co-authored-by: Isabel Matwawana <76437239+imatwawana@users.noreply.github.com>
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Señor Performo - Leandro Melendez
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@ -64,11 +64,16 @@ refs:
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destination: /docs/grafana/<GRAFANA_VERSION>/panels-visualizations/panel-editor-overview/#data-section
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- pattern: /docs/grafana-cloud/
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destination: /docs/grafana-cloud/visualizations/panels-visualizations/panel-editor-overview/#data-section
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data-transformation:
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- pattern: /docs/grafana/
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destination: /docs/grafana/<GRAFANA_VERSION>/panels-visualizations/panel-editor-overview/#data-section
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- pattern: /docs/grafana-cloud/
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destination: /docs/grafana-cloud/visualizations/panels-visualizations/panel-editor-overview/#data-section
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---
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# Time series
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Time series visualizations are the default way to visualize data points over intervals of time, as a graph. They can render series as lines, points, or bars and are versatile enough to display almost any time-series data.
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Time series visualizations are the default way to show the variations of a set of data values over time. Each data point is matched to a timestamp and this _time series_ is displayed as a graph. The visualization can render series as lines, points, or bars and it's versatile enough to display almost any type of [time-series data](https://grafana.com/docs/grafana/<GRAFANA_VERSION>/fundamentals/timeseries/).
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{{< figure src="/static/img/docs/time-series-panel/time_series_small_example.png" max-width="1200px" alt="Time series" >}}
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@ -76,6 +81,14 @@ Time series visualizations are the default way to visualize data points over int
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You can migrate from the legacy Graph visualization to the time series visualization. To migrate, open the panel and click the **Migrate** button in the side pane.
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{{< /admonition >}}
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A time series visualization displays an x-y graph with time progression on the x-axis and the magnitude of the values on the y-axis. This visualization is ideal for displaying large numbers of timed data points that would be hard to track in a table or list.
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You can use the time series visualization if you need track:
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- Temperature variations throughout the day
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- The daily progress of your retirement account
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- The distance you jog each day over the course of a year
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## Configure a time series visualization
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The following video guides you through the creation steps and common customizations of time series visualizations, and is great for beginners:
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@ -86,7 +99,72 @@ The following video guides you through the creation steps and common customizati
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## Supported data formats
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Time series visualizations require time-series data—a sequence of measurements, ordered in time, and formatted as a table—where every row in the table represents one individual measurement at a specific time. Learn more about [time-series data](https://grafana.com/docs/grafana/<GRAFANA_VERSION>/fundamentals/timeseries/).
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Time series visualizations require time-series data—a sequence of measurements, ordered in time, and formatted as a table—where every row in the table represents one individual measurement at a specific time. Learn more about [time-series data](https://grafana.com/docs/grafana/<GRAFANA_VERSION>/fundamentals/timeseries/).
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The dataset must contain at least one numeric field, and in the case of multiple numeric fields, each one is plotted as a new line, point, or bar labeled with the field name in the tooltip.
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### Example 1
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In the following example, there are three numeric fields represented by three lines in the chart:
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| Time | value1 | value2 | value3 |
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| ------------------- | ------ | ------ | ------ |
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| 2022-11-01 10:00:00 | 1 | 2 | 3 |
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| 2022-11-01 11:00:00 | 4 | 5 | 6 |
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| 2022-11-01 12:00:00 | 7 | 8 | 9 |
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| 2022-11-01 13:00:00 | 4 | 5 | 6 |
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If the time field isn't automatically detected, you might need to convert the data to a time format using a [data transformation](ref:data-transformation).
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### Example 2
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The time series visualization also supports multiple datasets. If all datasets are in the correct format, the visualization plots the numeric fields of all datasets and labels them using the column name of the field.
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#### Query1
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| Time | value1 | value2 | value3 |
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| ------------------- | ------ | ------ | ------ |
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| 2022-11-01 10:00:00 | 1 | 2 | 3 |
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| 2022-11-01 11:00:00 | 4 | 5 | 6 |
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| 2022-11-01 12:00:00 | 7 | 8 | 9 |
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#### Query2
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| timestamp | number1 | number2 | number3 |
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| ------------------- | ------- | ------- | ------- |
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| 2022-11-01 10:30:00 | 11 | 12 | 13 |
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| 2022-11-01 11:30:00 | 14 | 15 | 16 |
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| 2022-11-01 12:30:00 | 17 | 18 | 19 |
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| 2022-11-01 13:30:00 | 14 | 15 | 16 |
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### Example 3
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If you want to more easily compare events between different, but overlapping, time frames, you can do this by using a time offset while querying the compared dataset:
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#### Query1
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| Time | value1 | value2 | value3 |
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| ------------------- | ------ | ------ | ------ |
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| 2022-11-01 10:00:00 | 1 | 2 | 3 |
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| 2022-11-01 11:00:00 | 4 | 5 | 6 |
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| 2022-11-01 12:00:00 | 7 | 8 | 9 |
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#### Query2
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| timestamp(-30min) | number1 | number2 | number3 |
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| ------------------- | ------- | ------- | ------- |
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| 2022-11-01 10:30:00 | 11 | 12 | 13 |
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| 2022-11-01 11:30:00 | 14 | 15 | 16 |
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| 2022-11-01 12:30:00 | 17 | 18 | 19 |
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| 2022-11-01 13:30:00 | 14 | 15 | 16 |
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When you add the offset, the resulting visualization makes the datasets appear to be occurring at the same time so that you can compare them more easily.
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## Alert rules
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