QuantixAI turns raw time-series into clear forecasts you can explain and trust. In this walkthrough, you’ll create a baseline forecast, add what-if drivers, and learn how to read the outputs.
What you’ll get
- Baseline forecasts using robust statistical + ML models
- Scenario testing with exogenous features (e.g., marketing spend, holidays)
- Optional hierarchical reconciliation so rollups match the parts
Step 1 - Connect and explore
Upload your time series (CSV/XLSX) or connect to your source. Use the visualization panel to confirm trend, seasonality, and outliers. If needed, apply transformations or filters.
Step 2 - Build a baseline forecast
- Open Forecast → select your series (or a group).
- Choose horizon (e.g., 12 weeks / 6 months).
- Let QuantixAI auto-select models (statistical + ML) or pick your own.
- Generate forecast to view the mean and the confidence interval.
Step 3 - Add what-if drivers
Include external features like campaigns, pricing, weather, or holidays. Adjust their future values to run scenarios (“+20% marketing spend in Q4”). QuantixAI recomputes the forecast with those assumptions.
Step 4 - (Optional) Hierarchical reconciliation
When you forecast across levels (SKU → Category → Region → Total), enable reconciliation so the sums are consistent at every level.
Interpreting results
- Point forecast: your best estimate for each future period.
- Intervals: expected range - wider bands = more uncertainty.
- Feature impact (when using ML): how much each driver contributes to the forecast.
Tips
- Start with a simple baseline, then layer drivers.
- Compare scenarios side-by-side and export results for stakeholders.
- Re-train periodically as data or drivers change.
Ready to try it on your data? Start your 14-day free trial - no credit card required.