How to Conduct a Time-Series Analysis
Use a time-series analysis when you want to measure trends in outcomes and forecast future outcomes.
Step 1: Access the Time-Series Analysis Tool
- Click Time Series in the sidebar menu.
Step 2: Configure Your Analysis
- Select the date/time column (e.g., Date).
- Choose the outcome variable (target you want to predict, e.g., Revenue).
- Optionally, choose the entity variable (e.g., group with categories such as churn, quitting, fraud, with values 0 and 1).
- Choose predictor variables (features used for prediction, e.g., satisfaction, brand loyalty, burnout, address mismatch).
- Click Apply variables.
Step 3: Model the Time Series
- Click Enable statistical mode to run forecasting.
- Configure model parameters if needed (or use auto-selection).
- Click Run Analysis.
- The platform will:
- Fit the model to your historical data.
- Generate forecasts for future periods.
Step 4: Interpret Results, Take Action, and Monitor
- Look at Today’s Pulse for the revenue change and breakdown by group. Example:
- “An unfavorable trend has been detected for Revenue that steepened 21 days ago.”
- Compared to the last day, Revenue increased by +29.1k (+72.0%).
- Revenue was within the expected range of 34.5k to 87.6k, considering recent patterns.
- Scroll down to Averisys AI and click Results to review key outputs:
- Trend direction (upward, downward, stable).
- Forecasted values.
- Develop action plans based on insights (e.g., target customers at risk of churn, optimize pricing).
- Click Reports to see the executive summary.
- Click Publish this page to share results with stakeholders via a published page.
- Click Invite Users to invite stakeholders to share results via dashboards.
Tips & Best Practices
- Check data quality before analysis (no missing dates, consistent intervals).
- Use segmentation to analyze trends for different products or regions.
- Combine time-series analysis with other metrics (e.g., satisfaction, churn) for deeper insights.
If something does not work as described
Screens are updated as the platform changes, so a button may sit in a slightly different place than the wording above suggests. When that happens, the AI assistant inside the platform is the fastest way forward: it can see the page being worked on and answer in context, at any hour. Ask it what a message means, why a file was rejected, or which step comes next.
Most problems in this part of the workflow come from the data rather than the platform. Column headers that repeat, numbers stored as text, blank rows at the bottom of a file, and inconsistent category labels are the usual causes. Checking those four things resolves the majority of failed uploads and unexpected results before any further troubleshooting is needed.
For anything that still does not resolve, email support@averisysanalytics.com with the guide title, the step reached, and what appeared instead of the expected result. Including the file name and a screenshot usually means the issue is answered in the first reply. Related instructions are listed in the full platform guide, and common questions are collected in the support FAQ.