Questions tagged [mape]

The Mean Absolute Percentage Error (MAPE) is a point forecast accuracy measure. As a percentage, it can be compared between forecasts for time series on different scales, and it is easily interpreted. However, it is asymmetric (underforecasts' MAPEs are bounded at 100%, while overforecasts' are unbounded), potentially leading to biased forecasts. The MAPE is undefined if any actual is zero.

45 questions
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“Percentage” alternatives to MAPE

I'm aware of the problems of MAPE as a measurement, and particularly it's uselessness in the event of a time series where 0 is one of the many values of y. The downside to ditching MAPE in favour of ...
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Why am I getting better MAPEs when running an ARIMA model on a non-stationary time series (vs. a stationary one)?

I've been using ARIMA modelling to predict the number of orders a business receives. I have data for 3 years, and the time series shows a strong (uneven) upward trend, with increasing variance over ...
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What is the best point forecast for gamma distributed data?

I believe that the values I am forecasting are gamma distributed with shape $k>0$ and scale $\theta>0$. I need a point forecast (i.e., a one-number summary) that minimizes the expected error. ...
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What is the best point forecast for lognormally distributed data?

I believe that the values I am forecasting are lognormally distributed with log-mean $\mu$ and log-variance $\sigma^2$. I need a point forecast (i.e., a one-number summary) that minimizes the expected ...
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Correct approach to Mean Absolute Percentage Error

Currently I've been working on sales data. I have different categories that have very different figures (e.g. Groceries vs Technology), so I decided the best approach was the MAPE. The thing is, I ...
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MAPE of answer that is the division of two predicted values

Suppose I have predicted two sets of values (with decompositional methods) : Revenue with ~5.91% MAPE Capacity with ~0.92% MAPE And I'm suppose to derive a value, RPS as follows: RPS = Revenue / ...
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What statistical test do I need for comparing forecasted data with actual data?

I’m currently completing my dissertation and need to compare forecasted wave height to the actual wave height. However I am unsure what statistical test to use. Thanks, Jess
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Does normalisation affect the values of Mean Squared Error, Mean Absolute Percentage Error etc.?

I have a large amount of data, divided into folders and files. Each file has 56 features/columns and around 10,000 rows. The data is normalised (values between -1 and 1) and some of the features have ...
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How correctly overcome problem with an infinity MAPE? [duplicate]

Should I choose an other metric or is the way to handle this problem?
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why is the MSE error higher than MASE and MAPE?

I have a product price time series when I apply two models on them, I calculate all of MSE (Mean Squared Error), MASE (Mean Absolute Scaled Error), and MAPE (Mean Absolute Percentage Error). I noticed ...
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Interpretation of Theil's U2 Statistic - “Forecasting Methods and Applications” book

I was reading "Forecasting Methods and Applications" book and came across Theil's U statistic formula. I am facing difficulty in understanding the interpretation of Theil's U statistic. According to ...
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MAPE Value more than 100% [closed]

Im using Trend Analysis - Double Exponential Smoothing Plot (not seasonal and it has a trend testedly) to forecast the net amount of carrier switchers (using Mobile number portability) in the future ...
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MAPE yielding value of 1000+ for timeseries forecasting

I am comparing a model's outcome with the actual values for a span of 9 months. I am expecting a very large error, however my MAPE implementation yields 1404512.56 using python, pandas. Here is my ...
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Measuring accuracy for forecasting in R [closed]

I have a dataset of restaurant orders. In that data set I need to predict the outcome of the next 12 months, i.e. how much order will be given. using the following test ...
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MAPE vs R-squared in regression models

Usually regression models are evaluated using $R^2$. I understand this metric can be misleading too at times but as far as I understand the first parameter we look at is $R^2$. There is another ...
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Is MASE specified only to time series data

Will it be correct to use Mean absolute scaled error in non time series data? I've got a set which contains a lot of zeros, so errors like MAPE can not be used here. MASE based on difference between ...
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Gradient and hessian of the MAPE

I want to use MAPE(Mean Absolute Percentage Error) as my loss function. ...
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Relationship between forecast bias and accuracy for situations with constrained supply

Consider a forecast process which is designed to create unconstrained end-customer demand forecast. This means that the forecast generation process does not consider supply or distribution constraints....
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What are the shortcomings of the Mean Absolute Percentage Error (MAPE)?

The Mean Absolute Percentage Error (mape) is a common accuracy or error measure for time series or other predictions, $$\text{MAPE} = \frac{100}{n}\sum_{t=1}^n\frac{|A_t-F_t|}{A_t}\%,$$ where $A_t$ ...
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Is there an analog for the coefficient of determination with absolute error?

Typically to measure the predictive power of a model, R^2 is more useful than RMSE because a RMSE score isn't very meaningful without a basis of comparison. If you are using mean absolute error (MAE) ...
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Meaning of Min/Max Accuracy of a regression model

I'm trying to measure the accuracy of some linear regression models I fitted in R. I ran into this page offering a technique called Min_Max Accuracy which is ...
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Can MAPE Decrease while StdDev Increases?

Can a decrease in mean absolute percentage error (MAPE) be correlated with an increase in standard deviation of the error? Is that counter-intuitive? What about MAPE vs. mean absolute error (MAE)? I'm ...
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Is MAPE a good error measurement statistic? And what alternatives are there?

I have a time series that deals with rainfall. It is a period of 10 years (daily resolution), and covers climate variables. I'm going to feed the data into an Artificial Neural Network to predict the ...
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Compute MAPE with negative actual values

I am training a residual data with negative values through ANN. So I partition my data with 20 values for testing. But what I get in my MAPE is a negative value. Here is my data: ...
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What is the acceptable level of accuracy while doing Weekly Time Series Forecast

I'm doing a weekly time series analysis and I'm generally getting a mape of 35% is that ok according to the industry standard?
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Is R2 useful to determine the accuracy of VARX model?

I have a VARX model with 3 endogenous variables in 2nd differences. Each of 3 equations has very low $R^2$ (about 0,02), but the model gives good forecast (MAPEs are about 2%). Can I neglect low $R^2$ ...
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Which prediction model is better for this rolling time series prediction?

The aim is to predict the breakdown time of a machine as a percentage of scheduled hours for the next day. So my time series looks like this, Break_down_percentage = 7%, 8%, 10%, 6%, 12 % etc. There ...
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Difference between forecasting accuracy and forecasting error?

I am working on a demand forecasting project and I am puzzled by the client's standards of forecast evaluation. The MAPE (Mean Absolute Percentage Error) with the sample data Forecast = 300 and Demand ...
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mape and mae from k-fold backtesting on time series

I performed a rolling window (i.e. do full sample, then next 4 observations until the last, and so on...) k-fold test for out of sample testing due to limited number of observations. From the MAPE ...
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ARIMA model with huge MAPE value

I have a question about the role of the MAPE in the ARIMA model optimization. For a daily time series I have found that the best model (using the Box-Jenkins approach) is an ARIMA(7,0,7)(0,0,0). If I ...
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Applying different time series models (ARIMA, HOLT-WINTER) on the basis of MAPE

I have a time series object calc_visit_ts. I want to apply the best fit time series model based on the MAPE value for each model. The issue I face is that the MAPE ...
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Best way to optimize MAPE

The MAPE is a metric that can be used for regression problems : $$\mbox{MAPE} = \frac{1}{n}\sum_{t=1}^n \left|\frac{A_t-F_t}{A_t}\right|$$ Where $A$ represents the actual value and $F$ the the ...
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Calculating MAPE [closed]

Is the below calculation of the Mean Absolute Percentage Error MAPE correct? I've included a workable example, but really the lines in question are these: ...
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estimating effect size with sMAPE in published results

I'm struggling to get the concept of effect size in the published forecasting literature. Most common metric that is used is the symmetric Mean Absolute Percentage Error (sMAPE). For instance see ...
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ANOVA / t-test to compare the errors of different models

I have two forecasting models, moving average and single exponential smoothing. The values of Mean Absolute Percentage Error (MAPE) is 5.2%, 5.8%. Since the difference of MAPE between the models are ...
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Why do the residual sum of squares and the mean absolute percentage error conflict with each other?

I carried out regression with 7th degree and 8th degree polynomials. As expected, the residual sum of squares for 8th degree polynomial regression is less than that of 7th degree polynomial regression....
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Weird forecasting results

I am testing a forecast framework which I have developed. I am using an ensemble model (mix of Linear, ETS, ARMA, Bayesian,) which was considerably better than mean forecasts when I was comparing them ...
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Minimizing symmetric mean absolute percentage error (SMAPE)

I am working on a forecasting application in which forecast errors are measured using the symmetric mean absolute percentage error:  SMAPE = \frac{1}{n} \sum\limits_{t=1}^n{\frac{|F_t - A_t|}{F_t + ...
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“Frequency” value for seconds/minutes intervals data in R

I'm using R(3.1.1), and ARIMA models for forecasting. I would like to know what should be the "frequency" parameter, which is assigned in the ts() function, if im ...
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Scale independent forecast error metric that works with changing signs

I am trying to analyze a quite large (~25,000 rows) dataset of cash flow forecasts. Receipts and expenses are aggregated, thus I may end up with the following data: ...
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Which is the best accuracy measuring criteria among rmse, mae & mape?

I have created training set and test set from my data. Then I performed auto.arima() and ets() in R on the training set to predict one-step ahead forecasts. These were then compared with the test set ...
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Mean absolute percentage error (MAPE) in Scikit-learn [closed]

How can we calculate the Mean absolute percentage error (MAPE) of our predictions using Python and scikit-learn? From the docs, we have only these 4 metric functions for Regressions: metrics....
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Metric to compare models?

Using R, I have developed three models: linear regression using lm(); decision tree using rpart(); k-nearest neighbor using <...