Regressors in action

Regressors help time series forecasting models “think ahead” by adding relevant external factors that influence the predicted outcome—such as promotions, price changes, or weather conditions. This makes your forecast more intelligent and better aligned with real-world scenarios.

📊 Key Takeaways from Our Case Study:

• Adding regressors to a sales forecast increased accuracy by 36%.

• Regressors helped the model understand why sales might spike or drop—beyond just seasonal patterns.

• They capture cause-effect relationships that time-based signals alone can’t fully explain.

• Results were visible immediately: better performance metrics like lower error rates (e.g., MAE, RMSE) and more accurate predictions during key events.

⚡ Before vs After Regressors

Below you can see a direct visual comparison of model performance before and after applying regressors:

✅ Why It Matter?

Regressors turn a simple forecast into a strategic tool. They give your model context about what’s happening in the business, allowing it to:

• Anticipate demand shifts more accurately

• Understand the impact of external drivers

• Provide more actionable insights for planning and decision-making

📖 Full Article

Read the full story and real-life example on our blog:

Forecasting That Thinks Ahead: How Regressors Improved Accuracy by 36%