Time series data, such as stock prices, weather patterns, or sensor readings, are often analyzed to understand trends, patterns, and anomalies over time. However, static charts and tables may not be enough to capture the complexity and dynamics of time series data. How can you create interactive time series visualizations for exploratory analysis?
Interactive visualization allows you to manipulate and explore time series data in a more intuitive and engaging way. You can zoom in and out, filter, highlight, compare, and drill down on specific time periods, variables, or segments of interest. You can also add interactivity to your charts, such as tooltips, annotations, legends, or sliders, to provide more context and information. Interactive visualization can help you discover insights, communicate findings, and tell stories with time series data.
Interactive visualizations are always effective in terms of keeping the audience engaged with the data and enabling more insights. Especially, in time series data, interactive viz is very useful as it allows you to zoom in to a shorter time frame or zoom out to a longer time frame for analyzing the seasonality and trend in the plots.
Time series data, while extremely complex because of the time "factor", also benefits from this complexity since it allows data analysts, engineers, scientists etc. to build very in-depth dashboards and reports on it. This doesn't mean the dashboard/report must be in-depth as it all depends on the needs of the end-user. So more often than not a data slicer alongside a line chart will suffice.
When creating interactive time series visualizations, there are many tools and libraries to choose from based on your preferences, needs, and skills. Plotly, a Python library, supports a wide range of interactive charts and integrates with other libraries like pandas and dash. Bokeh is another Python library that enables interactive web-based visualization with widgets, tools, callbacks, and server integration. D3.js is a JavaScript library that enables custom and dynamic data-driven visualizations using HTML, SVG, and CSS. It can handle large and complex data sets with interactive components such as brushes, zooms, transitions, and events.
While Python and JavaScript tools offer the most flexibility when it comes to building visuals with time series data, one cannot dismiss Power BI's capabilities in this regard as well. Beyond its forecasting tool, it's rather easy building great visuals for time series data in PBI given how many styles of graphs there are to use in it. Besides the tool is constantly adding new features and optimising old ones that can further improve forecasting, such as Azure Machine Learning.
In my opinion, Python and its libraries provide more flexibility to make plots and graphs to visualize the data. Azure's Power BI and Tableau are great tools for code-free visualizations with drag-and-drop properties and neat labels. Also, this can be extended to create a dashboard of multiple interactive visualizations.
Creating interactive time series visualizations is not only about selecting the appropriate tool, but also designing the right chart. When picking a chart type, consider what data and analysis goals you have. For instance, line charts are suitable for displaying trends and changes over time, while scatter plots are ideal for showing correlations and outliers. Additionally, use color, size, shape, and opacity to encode additional dimensions or categories of your data. However, be careful not to overwhelm the viewer with too many or conflicting colors. You can also add interactivity elements to enhance the user experience and understanding of your data. Just be sure to avoid unnecessary or redundant features that may clutter or slow down the visualization. Finally, test and refine your interactive visualization by checking its functionality, usability, and aesthetics. Request feedback from your target audience and make adjustments as needed.
As said, time series data is complex but its visualization don't need to be. It's not always the case that the more we have, the better. Therefore dashboards and reports built with time series data must always have the end-users in mind.
Once you have created your interactive time series visualization, you may want to share it with others, such as colleagues, clients, or an online community. Depending on the purpose and audience, there are different options for sharing your visualization. For example, you can export it as a static image or HTML file and attach it to an email, report, or presentation. This is a simple and quick way to share your visualization, but it may lose some of the interactivity and quality. Alternatively, you can embed it into a web page or blog post using an iframe or script tag. This is more interactive and dynamic but may require web development skills and hosting services. Lastly, you can publish it on a cloud platform or dashboard tool like Plotly Cloud, Bokeh Server, or D3.js Observable. This is the most advanced and professional way to share your visualization but may involve costs and security issues.
Once again, Power BI might be an underrated tool when it comes to time series data reporting. You can very well share PBI reports either on desktop or online versions, and even build online dashboards which only have the key visuals for the end-user to view.
Creating interactive time series visualizations is a skill that can be beneficial for data engineers, as it allows them to effectively and efficiently explore, analyze, and communicate time series data. If you are interested in learning more about this topic, some helpful resources include Interactive Data Visualization for the Web by Scott Murray, Python Plotting with Bokeh by Bryan Van de Ven, and the Plotly Python Graphing Library by Plotly. All of these resources provide comprehensive tutorials on how to create interactive web-based visualizations with D3.js, as well as cover the basics and advanced features of Bokeh for interactive data visualization.