Zansa › Answers › Forecasting and data science
What is a forecasting baseline and why do I need one?
A baseline is a deliberately simple forecast, such as repeating last period's value or last year's same-week value, that any fancier method must beat. It gives context to accuracy numbers: a 20 percent error means little until you know a naive method achieves 22 or 40 percent.
Without a baseline you cannot tell whether a complex model adds value. Many sophisticated models turn out barely better than a seasonal naive forecast.
Good baselines include:
- Naive: next period equals the last one.
- Seasonal naive: next period equals the same period last cycle.
- Moving average of recent periods.
Measure the baseline using the same backtest and the same metric as the model. If the model does not clearly win, keep the baseline. Zansa compares every machine-learning forecast against such a baseline before recommending it.
Want this applied to your business? Zansa builds dashboards and forecasts for small and mid-sized companies worldwide. Start with a free intro call.
Book a call