If you are forecasting a time series that contains an outlier, there is a danger that the outlier could have a significant impact on the forecast. One solution to this problem is to screen the historical data for outliers and replace them with more typical values prior to generating the forecasts. This process is referred to as outlier detection and correction.
An outlier is a data point that falls outside of the expected range of the data (i.e., it is an unusually large or small data point). If you ignore outliers in your data, there is a danger that they can have a significant adverse impact on your forecasts. This article surveys three different approaches to forecasting data containing outliers, discusses the pros and cons of each and makes recommendations about when it is best to use each approach.
Preparing forecasts using data that contain one or more unusually large or small demand periods can be challenging. Depending on your forecasting approach, these “outliers” can have a significant impact on your forecasts. This article surveys three different approaches to forecasting data containing unusual demand periods, discusses the pros and cons of each and recommends when it is best to use each approach.
The recording of our recent educational one-hour Webinar is now available for viewing on-demand
Learn about helpful tools that make it easier for you to focus on the specific items that need your attention:
- Outlier detection for highlighting anomalies in historic data.
- Exception reports for identifying problems in the forecasts.
- Item reports for quickly defining and reviewing subsets of your data.
- Filtering & ABC Categorization (Pareto Analysis) for classifying items, allowing you to focus on the items that have the greatest impact on your bottom line.
You can register now for our next live Webinar Integrating Statistical Forecasts with Other Input to Create a Demand Plan for S&OP