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more stack exchange communities company blog **Stack Exchange** Inbox Reputation and Badges sign up log in tour help Tour Start here for a quick overview of the site Help Center Detailed 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 What happens if one brings more than 10,000 USD with them into the US? For example, what if the error is 90% on two products; one averages 1 million units per month, and the other 10 units per month. http://edvinfo.com/mean-absolute/mean-absolute-percentage-of-error.html

WikipediaÂ® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. Watch Queue Queue __count__/__total__ Find out whyClose Forecast Accuracy Mean Average Percentage Error (MAPE) Ed Dansereau SubscribeSubscribedUnsubscribe901901 Loading... The MAPE and MAD are the most commonly used error measurement statistics, however, both can be misleading under certain circumstances. 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

MAPE is known to give skewed results for data that is very close to 0. I don't know what MAPE means or what "forecasting" is.. This example is obvious in the first table.

The MAPE is scale **sensitive and** care needs to be taken when using the MAPE with low-volume items. Mean squared deviation (MSD) A commonly-used measure of accuracy of fitted time series values. The difference between At and Ft is divided by the Actual value At again. Mape India in Transportation Engineering from the University of Massachusetts.

The equation is: where yt equals the actual value, equals the forecast value, and n equals the number of forecasts. Weighted Mape Joshua Emmanuel 29,437 views 4:52 Forecasting - Measurement of error (MAD and MAPE) - Example 2 - Duration: 18:37. up vote 0 down vote favorite I made a model based on raw data and I wanted to check how accurate the model was using the Mean Absolute Percentage Error. http://www.forecastpro.com/Trends/forecasting101August2011.html East Tennessee State University 42,959 views 8:30 How to work out percent error - Duration: 2:12.

Notice that because "Actual" is in the denominator of the equation, the MAPE is undefined when Actual demand is zero. Mape In R Dr. The MAPE is often expressed as a percentage, that is, $0.057$ would be reported as $5.7\%$. There is a very long list of metrics that different businesses use to measure this forecast accuracy.

UV lamp to disinfect raw sushi fish slices Want to make things right, don't know with whom more hot questions question feed about us tour help blog chat data legal privacy Have you zero meaned the data yourself? Mean Absolute Percentage Error Excel These papers also show that the most indicative measure would be geometric mean of Relative MAEs or geometric mean of MAD/Mean ratios: http://www.researchgate.net/publication/282136084_Measuring_Forecasting_Accuracy_Problems_and_Recommendations_(by_the_Example_of_SKU-Level_Judgmental_Adjustments) http://www.researchgate.net/publication/284947381_Forecast_Error_Measures_Critical_Review_and_Practical_Recommendations Sujit Singh December 15, 2015 at 8:48 Google Mape SUBSCRIBE!

Are you dividing by the number of samples you've used to get your MAPE, or are you simply summing them? http://edvinfo.com/mean-absolute/mean-absolute-error-excel.html Show more Language: English Content location: United States Restricted Mode: Off History Help Loading... More formally, Forecast Accuracy is a measure of how close the actuals are to the forecasted quantity. Minitab.comLicense PortalStoreBlogContact UsCopyright Â© 2016 Minitab Inc. Mean Percentage Error

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 I wanted to suggest some recent papers that discover additional effects that render MAPE quite difficult to interpret. Have you tried removing the data values very close to 0 as a sanity check? –Katie Nov 23 '15 at 2:31 I just looked at the values near 0 his comment is here IntroToOM 116,704 views 3:59 Forecast Function in MS Excel - Duration: 4:39.

Where are sudo's insults stored? Mean Absolute Scaled Error The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted pointsn. Because the GMRAE is based on a relative error, it is less scale sensitive than the MAPE and the MAD.

In order to avoid this problem, other measures have been defined, for example the SMAPE (symmetrical MAPE), weighted absolute percentage error (WAPE), real aggregated percentage error, and relative measure of accuracy Personally I am one of the detractors of the MAPE, but not for its asymmetry. Consider that even fast moving consumer goods companies these days typically have over 90% of SKU-locations in the long tail (i.e. Wmape However, for the same product, a miss of 10 units is equally important in both cases.

The reason the MAPE is different between customers 1 and 2 is because the actual demand is different. When done right, this allows a business to keep the customer happy while keeping the costs in check. 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. weblink Planning: »Budgeting »S&OP Metrics: »DemandMetrics »Inventory »CustomerService Collaboration: »VMI&CMI »ABF Forecasting: »CausalModeling »MarketModeling »Ship to Share For Students MAPE and Bias - Introduction MAPE stands for Mean Absolute Percent Error -

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 Stats Doesn't Suck 13,651 views 12:05 Forecast Accuracy: Mean Absolute Error (MAE) - Duration: 1:33. rows or columns)). Call: +1 877 722 7627|info@arkieva.com BlogPricingContact SolutionsBy RoleExecutivesPlannersIT ManagersIndustriesDemand PlanningCasual ForecastingCollaborative ForecastingLife Cycle ManagementPerformance ManagementSegmentationStatistical ForecastingSupply PlanningRough Cut Capacity Planning (RCCP)Replenishment PlannerSupply PlannerSchedulingOrder Promising EngineS & OP CentralCollaborative PlanningSales CentralSales PredictorWhat-If