As time series data analysis becomes more important in intersectoral applications, so does the visualization of time series data. The more easily accessible and shared between teams, the more valuable the data is. A single time series graph or dashboard is worth several written reports by providing a visual snapshot of the time course of a particular parameter set.
What can we learn from time series visualization
Visualizing time series data helps you detect patterns, outliers that violate those patterns, whether the data is stationary or non-stationary, and whether there is a correlation between variables. For example, a time series line chart (also known as a time plot) displays values for time. Similar to the xy graph, but only displays time on the x-axis. Time series graphs can take a more complex form that provides more context for your data.
Time series data can be queried and graphed in dashboards that span different visualization types. The visualization type you use depends on the visualization type that best suits your use case. Time series graphs visually emphasize the behavior and patterns of your data. You can easily identify patterns such as trends, seasonality, and correlation.
Let’s take a look at some of the tools for graphing time series data and some of their visualization capabilities.
Time series graph creation tool
Time series charting tools often come with preconfigured dashboards to make it easier to get started.Open source projects like InfluxDB (Disclosure: I work for InfluxData), a time series platform with a built-in dashboard engine) and Grafana Is a common choice for visualizing time series data and provides different types of time series plots that make observations meaningful and easy to interpret. Because Grafana is integrated with InfluxDB, you often use a combination of the two platforms to visualize data from a variety of data sources, making it easier to monitor sensors, systems, and networks.
Visualization of time series data using InfluxDB
The built-in InfluxDB UI is the entire package for working with time series data in InfluxDB Cloud or InfluxDB OSS. The UI provides users with no-code tools to start writing data to InfluxDB, visual scripting and querying tools, the ability to perform data transformation tasks, alerting tools, and more. Of course, the InfluxDB UI also provides users with powerful tools for building custom dashboards. For example:
- InfluxDB can visualize time series data using custom graphs in graph libraries such as Plotly.js, Rickshaw and Dygraphs.
- InfluxDB templates, a set of tools that include a packager and a set of pre-made dashboards, allow users to share their monitoring expertise.
Visualization types available in the InfluxDB UI include band charts, gauge charts, line and bar charts, single charts, heatmaps, histograms, mosaics, scatter charts, and tables.
Visualization of time series data using Grafana
The process of setting up a Grafana dashboard and integrating it with different data sources is easy. Grafana comes with a feature-rich data source plugin for InfluxDB. The plugin includes a custom query editor that supports annotations and query templates.
Grafana has a rich set of graphing features that provide a high degree of customization for building and editing dashboards. The features are:
- Dynamic and reusable dashboard
- Data exploration with ad hoc queries and dynamic drilldown
- Log search
- Visually define alert rules
- Annotations to display event metadata and tags
You can use plugins to import data from external data sources and return the data in a format that Grafana understands. Various data sources are integrated with Grafana to create Grafana dashboards that allow users to visualize time series analysis and extract insights.
A combination of data visualization and 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 a way that seems appropriate. Statistical, dynamic statistics, financial momentum, mathematics, and even geo-time functions need to be easily applied to time series data to prepare the data for meaningful data visualization. flux, InfluxData Features The query and scripting language allows InfluxDB users to do all this.
Flux allows users to create powerful geo-temporal visualizations. Flux also allows users to create custom functions for anomaly detection. This blog post Using Flux for anomaly detection highlights why powerful data visualization tools require complementary analytical tools. It is almost impossible to find anomalous series in a similar time series collection of this data.
However, the median absolute deviation flux function, a custom anomaly detection algorithm, helps users discover and visualize the resulting anomalies in their datasets.
Anais Dotis-Georgiou is an InfluxData developer advocate with a passion for beautifying data using data analytics, AI and machine learning. She takes the data she collects and applies a combination of research, research, and engineering to transform the data into something that is functional, valuable, and aesthetically pleasing. When she’s not behind the screen, you can find her drawing, stretching, boarding, and chasing a soccer ball.
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How to visualize time series data
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