This can also be intuitively explained as the average absolute deviation relative to the average unit demand. In other words, this is the percent Mean absolute deviation or PMAD. MAPE can be defined as the volume weighted absolute error relative to the total actual demand. small numbers don’t heavily influence this calculation. So equation 1 for MAPE is not the recommended solution, although many academics use this as a model diagnostic.Įquation 2 gives you the correct MAPE as used by the Supply Chain practitioners. => This gives you the correct MAPE weighted by volume.Īveraging percentages can give you strange numbers. => This calculates the average of the Percentages. Thank you very much for your help, I really appreciate it in advance.Here are the equations that you originally sent through email. And I would also like to know why RMSE and MAPE values are so different here.
But my main question is when I check the accuracy if I choose RMSE I have to pick the harmonic regression one and if I choose MAPE I have to pick neural network model. They all seem fine based on Ljung-Box test but they somehow failed to capture the wiggly form of the time-series here which I don't know how important it is. I also put the Ljung-Box test and the plot of predicted values here for each: # Arimaĭata: Residuals from Regression with ARIMA(2,1,1) errors The time series look like this, with heavy seasonal and cyclic pattern: So far I've tried arima, Harmonic regression with arima error, neural network and in the end I would like to decide which one has been better fitted to my raw data. I have a weekly times series for which I would like to find the best fit model.
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