How To Calculate Mean Absolute Percentage Error In Excel
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Interpretation of these statistics can be tricky, particularly when working with low-volume data or when trying to assess accuracy across multiple items (e.g., SKUs, locations, customers, etc.). how to calculate mape This installment of Forecasting 101 surveys common error measurement statistics, examines the pros how to calculate forecast error in excel and cons of each and discusses their suitability under a variety of circumstances. The MAPE The MAPE (Mean Absolute Percent weighted mape Error) measures the size of the error in percentage terms. It is calculated as the average of the unsigned percentage error, as shown in the example below: Many organizations focus primarily on the google mape MAPE when assessing forecast accuracy. Most people are comfortable thinking in percentage terms, making the MAPE easy to interpret. It can also convey information when you don’t know the item’s demand volume. 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 demand volume.
Mean Percentage Error
The MAPE is scale sensitive and should not be used when working with low-volume data. Notice that because "Actual" is in the denominator of the equation, the MAPE is undefined when Actual demand is zero. Furthermore, when the Actual value is not zero, but quite small, the MAPE will often take on extreme values. This scale sensitivity renders the MAPE close to worthless as an error measure for low-volume data. The MAD The MAD (Mean Absolute Deviation) measures the size of the error in units. 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 item. However, if you aggregate MADs over multiple items you need to be careful about high-volume products dominating the results--more on this later. Less Common Error Measurement Statistics The MAPE and the MAD are by far the most commonly used error measurement statistics. There are a slew of alternative statistics in the forecasting literature, many of which are variations on the MAPE and the MAD. A few of the more important ones are listed below: MAD/Mean Ratio. The MAD/Me
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Phone: +1 (888) forecast error calculation 427-9486+1 (312) 257-3777 Contact Us Home >> Support >> Documentation >> NumXL >> Reference Manual >> Descriptive Stats >> mean forecast error MAPE MAPE Calculates the mean absolute percentage error (Deviation) function for the forecast and the eventual outcomes. Syntax MAPEi(X, Y, Ret_type) X is the original (eventual outcomes) time series sample http://www.forecastpro.com/Trends/forecasting101August2011.html data (a one dimensional array of cells (e.g. rows or columns)). Y is the forecast time series data (a one dimensional array of cells (e.g. rows or columns)). Ret_type is a switch to select the return output (1=MAPE (default), 2=Symmetric MAPE (SMAPI)). Order Description 1 MAPE (default) 2 SMAPE Remarks MAPE is also referred to as MAPD. The time series is homogeneous http://www.spiderfinancial.com/support/documentation/numxl/reference-manual/descriptive-stats/mape or equally spaced. 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. 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. The two time series must be identical in size. 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 When calculating the average MAPE for a number of time series, you may encounter a problem: a few of the series that have a very high MAPE might distort a comparison between the average MAPE of a time series fitted with one method compared to the average MAPE when using another method. 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 (ROMA). The symmForums Excel Questions Function to calculate MAPE Results 1 to 3 of 3 Function to calculate MAPEThis is a discussion on Function to calculate MAPE within the Excel Questions forums, part of the Question Forums category; Hi http://www.mrexcel.com/forum/excel-questions/20193-function-calculate-mape.html there, I'm trying to write a function in excel which will calculate the mean absolute http://math.stackexchange.com/questions/241068/mean-absolute-percentage-error percentage error (MAPE) for ... LinkBack LinkBack URL About LinkBacks Bookmark & Share Digg this Thread!Add Thread to del.icio.usBookmark in TechnoratiTweet this thread Thread Tools Show Printable Version Display Linear Mode Switch to Hybrid Mode Switch to Threaded Mode Sep 2nd, 2002,09:00 AM #1 Lucas in London Board Regular Join Date Jun 2002 Posts 88 Hi there, I'm how to trying to write a function in excel which will calculate the mean absolute percentage error (MAPE) for a variable. Basically this is a measure of forecast accuracy, which compares forecasts for a variable against actual values. For those not familiar with this measure, basically I need a function that will calculate the absolute percentage difference between the values for two variables and return the average or sum of these: For example say we how to calculate have two variables (X and Y) with following values: X Y The pct difference = 7 7 0 10 11 10 15 21 40 Average = 16.6 MAPE = 16.6 I know how to do these calculations within a spreadsheet but I want to do using a custom function. Also I know how to create inputboxes for the user to select the ranges for X and Y but I just haven't got a clue how to write the actual basic formula to calculate differences in values between two arrays and calculate statistics on these. I hope someone out there can help me. Many Thanks Lucas in currently a sunny London Share Share this post on Digg Del.icio.us Technorati Twitter Reply With Quote Sep 2nd, 2002,10:11 AM #2 Andrew Poulsom MrExcel MVPModerator Join Date Jul 2002 Posts 73,092 Insert a module in your workbook and paste the following code: Code: Function MAPE(Original As Range, Revised As Range) Dim Divisor As Long Dim x As Long Dim Change As Double Dim TotalChange As Double Divisor = Original.Rows.Count Change = 0 TotalChange = 0 For x = 1 To Divisor Change = Abs(((Revised.Cells(x, 1) / Original.Cells(x, 1)) - 1) * 100) TotalChange = TotalChange + Change Next x MAPE = TotalChange / Divisor End Function Assuming your X data is in cells
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Mathematics Questions Tags Users Badges Unanswered Ask Question _ Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Mean Absolute Percentage Error up vote 0 down vote favorite I am trying to work on some Excel exercises I found to prepare for an upcoming course and I stumbled upon some questions and terms that I am not familiar with. Anyone know how to do these questions? I don't know what MAPE means or what "forecasting" is.. Not familiar with the strange formulas in this question either...Hopefully someone knows something about this. statistics share|cite|improve this question asked Nov 20 '12 at 3:42 Raynos 71341538 add a comment| 1 Answer 1 active oldest votes up vote 1 down vote accepted Suppose that you weigh $9$ people, using a not very good scale S. Let $w_1,w_2,\dots,w_9$ be the actual weights of the people, say measured using a high precision scale, and let $m_1,m_2,\dots,m_9$ be their measured weights using our low quality scale. 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 $x$. Formally, it is defined by $|x|=x$ if $x\ge 0$, and $|x|=-x$ if $x\lt 0$. For example, $|4|=4$ and $|-4.7|=4.7$. So $|w_1-m_1|$ measures the "error" made in weighing the first person. And $\dfrac{|w_1-m_1}{w_1}$ measures the relative error made in weighing. 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 $5$ pound error in the weight of a year-old child might be. For the MAPE, we find the average relative error. The MAPE is often expressed as a percentage, that is, $0.057$ would be reported as $5.7\%$. Suppose we are making predictions (forecasts) about monthly sales, January to September. Then the $w_i$ would be the actual sales. The $m_i$ would be the predicted sales. Then the MAPE is a measure of by wh