The Most Common Forecasting Challenge: Non-Constant Variance

Many forecasting models assume constant variance. Real-world sales data rarely follows that rule.

What Is Non-Constant Variance?

When fluctuations increase as demand grows, variance becomes unstable. This leads to unreliable confidence intervals and biased forecasts.

How Data Transformation Helps

• Log transformation stabilizes variance
• Box-Cox improves model assumptions
• Scaling improves machine learning convergence

Why Ignoring Variance Hurts Forecast Accuracy

Models may look statistically valid but fail in production. Variance issues often appear only after deployment.

Our AI Sales Forecast Tool automatically detects and adjusts variance issues before forecasting.