EXCEL TRENDLINE IGNORE OUTLIERS SERIES
The timing of the distribution of free goods introduces outliers in the time series representing total demand. Consider the following three examples.Įxample A: A pharmaceutical company’s demand for a given drug consists of both prescription fills (sales) and free goods (e.g., samples distributed free of charge to physicians). Option #2: Separate the Demand StreamsĪt times, when the cause of an outlier is known, it may be useful to separate a time series into two different demand streams and forecast them separately. Ideally the thresholds would be calibrated empirically by experimenting with a subset of the data. In cases where an automated outlier detection and correction procedure must be used, (for example if the sheer number of forecasts to be generated precludes human review), then the thresholds for identifying and correcting an outlier should be set very high. Using an automated detection algorithm to identify potential candidates for correction is very useful however, the detected outliers should ideally be individually reviewed by the forecaster to determine whether a correction is appropriate.ģ. Outlier correction should be performed sparingly. If the cause of an outlier is known, alternative approaches (such as Option #2 and Option #3 below) should be considered prior to resorting to outlier correction.Ģ.
EXCEL TRENDLINE IGNORE OUTLIERS PRO
The screenshot above shows Forecast Pro TRAC’s ability to “correct” potential outliers. If the correction was not necessary, you may end up with poor forecasts and unrealistic confidence limits When you correct an outlier, you are rewriting the history to be smoother than it actually was and this will change the forecasts and narrow the confidence limits. Many forecasting solutions, including Forecast Pro, offer automated procedures for detecting outliers and “correcting” the history prior to forecasting.Ĭorrecting the history for a severe outlier will often improve the forecast however, if the outlier is not truly severe, corrections may do more harm than good. This process is often referred to as Outlier Correction. Option #1: Outlier CorrectionĪ simple solution to lessen the impact of an outlier is to replace the outlier with a more typical value prior to generating the 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. If you ignore outliers in your data, there is a danger that they can have a significant adverse impact on your forecasts. 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).
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