How one can visualize time sequence information

As time sequence information evaluation turns into extra important in purposes throughout sectors, so does visualizing time sequence information. The simpler information is to entry and the extra shareable it’s throughout groups, the extra worthwhile it turns into. A single time sequence graph or dashboard, by offering a visible snapshot of change over time for a given set of parameters, might be value a number of written stories.

What we are able to be taught from time sequence visualizations

Visualizing time sequence information might help detect patterns, outliers that defy these patterns, whether or not the info is stationary or non-stationary, and whether or not there may be correlation between the variables. For instance, a time sequence line graph (additionally referred to as a timeplot) shows values towards time. It’s much like x-y graphs however shows solely time on the x-axis. Time sequence graphs can take extra complicated kinds that present extra context concerning the information.

Time sequence information might be queried and graphed in dashboards spanning completely different visualization varieties. Which visualization sort to make use of will depend on which works greatest to your use case. Time sequence graphs visually spotlight the conduct and patterns of the info. They help you simply determine patterns like development, seasonality, and correlation.

Let’s assessment some instruments for graphing time sequence information and a few of their visualization capabilities.

Time sequence graphing instruments

Time sequence graphing instruments typically include pre-configured dashboards to facilitate getting began. Open supply initiatives like InfluxDB (disclosure: I work at InfluxData), a time sequence platform with a built-in dashboarding engine) and Grafana are fashionable selections for visualizing time sequence information and supply several types of time sequence plots that make noticed information significant and simpler to interpret. As Grafana integrates with InfluxDB, the 2 platforms are sometimes utilized in mixture to visualise information from numerous information sources and to facilitate sensor, system, and community monitoring.

Visualizing time sequence information with InfluxDB

The built-in InfluxDB UI is the entire package deal on the subject of working with time sequence information with InfluxDB Cloud or InfluxDB OSS. The UI supplies the person with every part together with no code instruments to get began writing information to InfluxDB, visible scripting and querying instruments, the flexibility to carry out information transformation duties, and alert creation instruments. Naturally, the InfluxDB UI additionally supplies the person with highly effective instruments for constructing customized dashboards. For instance:

  • InfluxDB can visualize time sequence information utilizing customized graphs from graphing libraries akin to Plotly.js, Rickshaw, and Dygraphs.
  • InfluxDB Templates, a set of instruments that features a packager and a set of pre-canned dashboards, permit customers to share their monitoring experience.

Visualization varieties obtainable by means of the InfluxDB UI embody band charts, gauge charts, line and bar graphs, single-stats graphs, heatmaps, histograms, mosaics, scatter plots and tables. 


The band chart shows the higher and decrease boundaries of teams of knowledge over time.


The heatmap reveals information distribution on x and y axes the place shade represents information level focus. 


The gauge graph reveals the one most up-to-date worth for a time sequence and the place it falls inside a spectrum.

Visualizing time sequence information with Grafana

The method of organising a Grafana dashboard and integrating it with numerous information sources is easy. Grafana ships with a feature-rich information supply plug-in for InfluxDB. The plug-in features a customized question editor and helps annotations and question templates.


Two Grafana cells visualize request times (right) and load times (left) with a line graph and bar plot respectively.


A Grafana dashboard used to monitor InfluxDB complete with visualizations about the average system load, queries executed, writes executed, series count, and more.


A Grafana dashboard used for application or website monitoring.

Grafana has a rich set of graphing features and offers a high level of customization for dashboard building and editing. Capabilities include:

  • Dynamic and reusable dashboards
  • Data exploration through ad-hoc queries and dynamic drilldown
  • Logs exploration
  • Visually defining alert rules
  • Annotations to view event metadata and tags

Plug-ins can be used to import data from external data sources and return the data in a format that Grafana understands. Various data sources integrate with Grafana to produce Grafana dashboards and help users to extract insights through visualizing time series analytics.

Combining data visualization with powerful analytics

The power of a data visualization solution depends on the companion analytics capabilities within the solution. Time series data scientists and analysts need the flexibility to transform their data in any way they see fit. They need to easily apply statistical, dynamic statistical, financial momentum, math, and even geotemporal functions to their time series data in order to prepare their data for meaningful data visualization. Flux, InfluxData’s functional query and scripting language, allows InfluxDB users to accomplish all of that.


An example of visualizing geotemporal data with the InfluxDB UI.

Flux enables users to create powerful geotemporal visualizations. Flux also enables users to build custom functions for anomaly detection. This blog post on using Flux for anomaly detection highlights why powerful data visualization tools require complementary analytics tools. It’s almost impossible to spot the anomalous series among the collection of like time series in this data:


However, the median absolute deviation Flux function, a custom anomaly detection algorithm, helps the user to uncover and visualize the resulting anomalies in that dataset:


Anais Dotis-Georgiou is a developer advocate for InfluxData with a passion for making data beautiful with the use of data analytics, AI, and machine learning. She takes the data that she collects and applies a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she is not behind a screen, you can find her outside drawing, stretching, boarding, or chasing after a soccer ball.

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