Furthermore, when the Actual value is not zero, but quite small, the MAPE will often take on extreme values. About the author: Eric Stellwagen is Vice President and Co-founder of Business Forecast Systems, Inc. (BFS) and co-author of the Forecast Pro software product line. One of the key questions in the forecasting process has to do with the measuring of the forecast accuracy. I think the most important problem is that, as was noticed above, MAPE does not represent accuracy under symmetric loss.

NumXL for Microsoft Excel makes sense of time series analysis: Build, validate, rank models, and forecast right in Excel Keep the data, analysis and models linked together Make and track changes Sign in to make your opinion count. 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. Mean absolute deviation (MAD) Expresses accuracy in the same units as the data, which helps conceptualize the amount of error.

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 It does not depend on scale and can apply easily to both high and low volume products. rows or columns)). The problem is that when you start to summarize MPE for multiple forecasts, the aggregate value doesn’t represent the error rate of the individual MPEs.

While a point value of the metric is good, focus should be on the trend line to ensure that the metric is improving over time. Subscribe to receive blog updates. The mean absolute percentage error (MAPE) is defined as follows:

Where: is the actual observations time series is the estimated or forecasted time series is the number of non-missing data points Related Posts Gallery Winning the Debate on Selecting a “Best of Breed" Supply Chain Solution.Published on Dec 13, 2012All rights reserved, copyright 2012 by Ed Dansereau Category Education License Standard YouTube License Show more Show less Loading... Then the mean absolute percentage error (MAPE) made by scale S is $$\frac{1}{9}\left(\frac{|w_1-m_1|}{w_1}+\cdots+\frac{|w_9-m_9|}{w_9}\right).$$ Note that in general $|x|$, the absolute value of $x$, measures the magnitude of $x$. Sign in to add this to Watch Later Add to Loading playlists... Calculating error measurement statistics across multiple items can be quite problematic.

It can also convey information when you dont know the items demand volume. Please try again later. Watch Queue Queue __count__/__total__ Find out whyClose Forecast Accuracy Mean Average Percentage Error (MAPE) Ed Dansereau SubscribeSubscribedUnsubscribe901901 Loading... For this reason, consider using Mean Absolute Deviation (MAD) alongside MAPE, or consider weighted MAPE (more on these in a separate post in the future).

The difference between At and Ft is divided by the Actual value At again. Sum of neighbours What are "desires of the flesh"? GMRAE. It is calculated as the average of the unsigned errors, as shown in the example below: The MAD is a good statistic to use when analyzing the error for a single

MAPE delivers the same benefits as MPE (easy to calculate, easy to understand) plus you get a better representation of the true forecast error. My CEO wants permanent access to every employee's emails. Excel Analytics 3,776 views 5:30 Forecasting - Measurement of error (MAD and MAPE) - Example 2 - Duration: 18:37. Browse other questions tagged statistics or ask your own question.

Loading... FacebookTwitterLinkedinRedditTumblrGoogle+PinterestVkEmail About the Author: Sujit Singh As COO of Arkieva, Sujit manages the day-to-day operations at Arkieva such as software implementations and customer relationships. Not familiar with the strange formulas in this question either...Hopefully someone knows something about this. This installment of Forecasting 101 surveys common error measurement statistics, examines the pros and cons of each and discusses their suitability under a variety of circumstances.

Sign in to make your opinion count. A few of the more important ones are listed below: MAD/Mean Ratio. Some companies have a tendency to over forecast which can very often be attributed to overconfidence bias. MAPE is asymmetric and reports higher errors if the forecast is more than the actual and lower errors when the forecast is less than the actual.

As an alternative, each actual value (At) of the series in the original formula can be replaced by the average of all actual values (Āt) of that series. What is the best way to upgrade gear in Diablo 3? 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 The symmetrical mean absolute percentage error (SMAPE) is defined as follows:

The SMAPE is easier to work with than MAPE, as it has a lower bound of 0% and an upperA potential problem with this approach is that the lower-volume items (which will usually have higher MAPEs) can dominate the statistic. East Tennessee State University 32,010 views 5:51 How to work out percent error - Duration: 2:12. Close Yeah, keep it Undo Close This video is unavailable. This is one reason why these organizations have adapted a different version of MAPE where the denominator is the forecast.

A few of the more important ones are listed below: MAD/Mean Ratio. The error on a near-zero item can be infinitely high, causing a distortion to the overall error rate when it is averaged in. Mean squared deviation (MSD) A commonly-used measure of accuracy of fitted time series values. I frequently see retailers use a simple calculation to measure forecast accuracy. It’s formally referred to as “Mean Percentage Error”, or MPE but most people know it by its formal. It

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. For the example you give it is indeed correct that 10 units error for demand of 90 is slightly worse than 10 units error for demand of 100. Often, we are more interested in relative error than in error, since an error of $5$ pounds in the weight of a $300$ pound person is not very important, while a