Outlier Removal: Clean the Noise Out of Your Data Without Wrecking the Forecast

If you work in demand forecasting, this probably sounds familiar: you have what seems like a solid history, you run the model and the result still doesn’t make sense. That’s exactly why we created this downloadable guide on outlier removal in forecasting.

Because, in many cases, the problem isn’t the model. It’s the data. More specifically, it’s the outliers.

These unusual values (demand spikes, one-time errors or abnormal behaviors) may look like isolated events, but their impact is often much larger than it appears. In fact, as this guide explains, a single out-of-place value can throw off the entire model.

This resource is designed to help you solve that problem in a practical way, without unnecessary complexity.

You’ll see why removing outliers is not as simple as applying a standard formula. Many companies still use classic methods like Z-score or IQR directly on historical data, but in real-world environments with seasonality, promotions or trends, that often creates more problems than it solves.

Why? Because those methods were not designed for time series. Used without context, they can flag “anomalies” that are actually completely normal for the business.

This document shows exactly that: why traditional approaches fall short and what to do instead.

Rather than working directly on the time series, we recommend a much more robust method: detecting outliers in the model residuals.

That may sound technical, but the guide explains it clearly and with a practical angle. Put simply, you first isolate the expected behavior (trend, seasonality and other structural patterns) and then identify which values truly deviate from what should have happened.

That shift in approach is critical. The objective isn’t just to “clean the data.” It’s to do it without losing valuable information or distorting the series.

What You’ll Find in This Guide

• What outliers mean in forecasting beyond the textbook definition.
• Why traditional methods don’t perform well with time series.
• Real examples of common mistakes in anomaly detection.
• A practical residual-based approach to identify outliers correctly.
• Key principles to improve data quality and increase forecast accuracy.

Download it now and start looking at your forecasts with a new level of precision.