Create a time series analysis

Use a time series analysis to identify trends in user behavior over time.

Analyze performance and experience metrics over time to identify trends, anomalies, and overall feature or segment performance in Splunk Digital Experience Analytics.

Use cases for time series analysis

• Monitor feature adoption, application performance trends, and detect degradation early.

• Analyze user experience changes over time to correlate with releases or incidents.
• Identify peak usage periods to optimize resource allocation.

1. From the Digital Experience Analytics Overview page, select the Analyses tab.
2. Select New analysis, and select the Time series option.
3. Select a time period to set the time range of the events you want to include as part of this analysis. For example, selecting Last 7 days will populate event data from the past 7 days.
4. Configure the time series analysis:
   1. Select the user segment users for your time series.
   2. Define the actions you want to track in the analysis using the pre-defined event templates, saved definitions, or RUM event data. To define an event definition, see Create and manage event definitions .
   3. (Optional) Select OR to add an additional event to track in your time series analysis.
5. Add a name to your time series analysis.
6. Preview the data from your analysis in the Preview panel.
7. Add another series to the chart to compare two series side-by-side.

Select your time series on the Analyses tab and view the results of your time series. Analyze the entire data set, or select a specific data point on the chart to view a curated list of sessions for the selected data, which can be further analyzed using session replay in Splunk Real User Monitoring (RUM). For more information, see Replay user sessions.