HinzufÃ¼gen MÃ¶chtest du dieses Video spÃ¤ter noch einmal ansehen? The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), measures the accuracy of a method for constructing fitted time series values in statistics. For forecasts which are too low the percentage error cannot exceed 100%, but for forecasts which are too high there is no upper limit to the percentage error. Go To: Retail Blogs Healthcare Blogs Retail The Absolute Best Way to Measure Forecast Accuracy September 12, 2016 By Bob Clements The Absolute Best Way to Measure Forecast Accuracy What

Letâ€™s start with a sample forecast.Â The following table represents the forecast and actuals for customer traffic at a small-box, specialty retail store (You could also imagine this representing the foot The equation is: where yt equals the actual value, equals the fitted value, and n equals the number of observations. A GMRAE of 0.54 indicates that the size of the current model’s error is only 54% of the size of the error generated using the naïve model for the same data So sMAPE is also used to correct this, it is known as symmetric Mean Absolute Percentage Error.

Order Description 1 MAPE (default) 2 SMAPE Remarks MAPE is also referred to as MAPD. All rights reserved. Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. Minitab.comLicense PortalStoreBlogContact UsCopyright Â© 2016 Minitab Inc.

For example, if the MAPE is 5, on average, the forecast is off by 5%. So you can consider MASE (Mean Absolute Scaled Error) as a good KPI to use in those situations, the problem is that is not as intuitive as the ones mentioned before. To overcome that challenge, youâ€™ll want use a metric to summarize the accuracy of forecast.Â This not only allows you to look at many data points.Â It also allows you to Melde dich an, um dieses Video zur Playlist "SpÃ¤ter ansehen" hinzuzufÃ¼gen.

Koehler. "Another look at measures of forecast accuracy." International journal of forecasting 22.4 (2006): 679-688. ^ Makridakis, Spyros. "Accuracy measures: theoretical and practical concerns." International Journal of Forecasting 9.4 (1993): 527-529 Privacy policy About Wikipedia Disclaimers Contact Wikipedia Developers Cookie statement Mobile view Forecasting 101: A Guide to Forecast Error Measurement Statistics and How to Use Multiplying by 100 makes it a percentage error. Calculating the accuracy of supply chain forecasts[edit] Forecast accuracy in the supply chain is typically measured using the Mean Absolute Percent Error or MAPE.

Wird verarbeitet... Retrieved from "https://en.wikipedia.org/w/index.php?title=Calculating_demand_forecast_accuracy&oldid=742393591" Categories: Supply chain managementStatistical forecastingDemandHidden categories: Articles to be merged from April 2016All articles to be merged Navigation menu Personal tools Not logged inTalkContributionsCreate accountLog in Namespaces Article Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. archived preprint ^ Jorrit Vander Mynsbrugge (2010). "Bidding Strategies Using Price Based Unit Commitment in a Deregulated Power Market", K.U.Leuven ^ Hyndman, Rob J., and Anne B.

powered by Olark live chat software Scroll to top Demand Planning.Net: Are you Planning By Exception? Ret_type is a switch to select the return output (1=MAPE (default), 2=Symmetric MAPE (SMAPI)). Wird geladen... All error measurement statistics can be problematic when aggregated over multiple items and as a forecaster you need to carefully think through your approach when doing so.

SMAPE. Outliers have a greater effect on MSD than on MAD. Calculating an aggregated MAPE is a common practice. The SMAPE (Symmetric Mean Absolute Percentage Error) is a variation on the MAPE that is calculated using the average of the absolute value of the actual and the absolute value of

Mean absolute percentage error (MAPE) Expresses accuracy as a percentage of the error. Last but not least, for intermittent demand patterns none of the above are really useful. For forecasts of items that are near or at zero volume,Â Symmetric Mean Absolute Percent Error (SMAPE)Â is a better measure.MAPE is the average absolute percent error for each time period or forecast Please help improve this article by adding citations to reliable sources.

Calculating error measurement statistics across multiple items can be quite problematic. This post is part of the Axsium Retail Forecasting Playbook, a series of articles designed to give retailers insight and techniques into forecasting as it relates to the weekly labor scheduling Recognized as a leading expert in the field, he has worked with numerous firms including Coca-Cola, Procter & Gamble, Merck, Blue Cross Blue Shield, Nabisco, Owens-Corning and Verizon, and is currently Email: Please enable JavaScript to view.

Because this number is a percentage, it can be easier to understand than the other statistics. Definition of Forecast Error Forecast Error is the deviation of the Actual from the forecasted quantity. What is the impact of Large Forecast Errors? A potential problem with this approach is that the lower-volume items (which will usually have higher MAPEs) can dominate the statistic.

Syntax MAPEi(X, Y, Ret_type) X is the original (eventual outcomes) time series sample data (a one dimensional array of cells (e.g. Another approach is to establish a weight for each item’s MAPE that reflects the item’s relative importance to the organization--this is an excellent practice. For example, telling your manager, "we were off by less than 4%" is more meaningful than saying "we were off by 3,000 cases," if your manager doesn’t know an item’s typical Multiplying by 100 makes it a percentage error.

This little-known but serious issue can be overcome by using an accuracy measure based on the ratio of the predicted to actual value (called the Accuracy Ratio), this approach leads to So we constrain Accuracy to be between 0 and 100%. Fax: Please enable JavaScript to see this field. For forecasts which are too low the percentage error cannot exceed 100%, but for forecasts which are too high there is no upper limit to the percentage error.

Error above 100% implies a zero forecast accuracy or a very inaccurate forecast. Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. He consults widely in the area of practical business forecasting--spending 20-30 days a year presenting workshops on the subject--and frequently addresses professional groups such as the University of Tennessee’s Sales Forecasting VerÃ¶ffentlicht am 13.12.2012All rights reserved, copyright 2012 by Ed Dansereau Kategorie Bildung Lizenz Standard-YouTube-Lizenz Mehr anzeigen Weniger anzeigen Wird geladen...

As stated previously, percentage errors cannot be calculated when the actual equals zero and can take on extreme values when dealing with low-volume data. Wird geladen... For a plain MAPE calculation, in the event that an observation value (i.e. ) is equal to zero, the MAPE function skips that data point. Hoover, Jim (2009) "How to Track Forecast Accuracy to Guide Process Improvement", Foresight: The International Journal of Applied Forecasting.

The MAPE is scale sensitive and should not be used when working with low-volume data. Next Steps Watch Quick Tour Download Demo Get Live Web Demo Mean absolute percentage error From Wikipedia, the free encyclopedia Jump to: navigation, search This article needs additional citations for verification. Unsourced material may be challenged and removed. (December 2009) (Learn how and when to remove this template message) The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation This statistic is preferred to the MAPE by some and was used as an accuracy measure in several forecasting competitions.

In my next post in this series, Iâ€™ll give you three rules for measuring forecast accuracy.Â Then, weâ€™ll start talking at how to improve forecast accuracy. Notice that because "Actual" is in the denominator of the equation, the MAPE is undefined when Actual demand is zero. The statistic is calculated exactly as the name suggests--it is simply the MAD divided by the Mean. The error on a near-zero item can be infinitely high, causing a distortion to the overall error rate when it is averaged in.

A singularity problem of the form 'one divided by zero' and/or the creation of very large changes in the Absolute Percentage Error, caused by a small deviation in error, can occur.